Systems Engineering Research Articles

Electric Vehicle Thermal System Concept Development for Multiple Variants Using Digital Prototype and AI

The automotive industry is experiencing a surge in system complexity driven by the ever-growing number of interacting components, subsystems, and control systems. This complexity is further amplified by the expanding range of component options available to original equipment manufacturers (OEMs). OEMs work in parallel on more than one vehicle model, with multiple vehicle variants for each vehicle model. With the increasing number of vehicle variants needed to cater to diverse regional needs, development complexity escalates. To address this challenge, modern techniques like Model-Based Systems Engineering (MBSE), digitalization, and Artificial Intelligence (AI) are becoming essential tools. These advancements can streamline concept development, optimize thermal and HVAC system design across variants, and accelerate the time-to-market for next-generation EVs. The development of battery electric vehicles (BEVs) needs a strong focus on thermal management systems (TMSs) and heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in maintaining optimal battery temperature, maximizing range and efficiency, and ensuring passenger comfort. This article proposes a digital prototype (DP) and AI-based methodology to specify BEV thermal system and HVAC system components in the concept phase. This methodology uses system and variant thinking in combination with digital prototype (DP) and AI to verify BEV thermal system architecture component specifications for future variants without extensive simulation. A BEV cabin cooling requirement of 22 °C to be achieved within 1800s at a high ambient temperature (45 °C) is required, and its verification is used to prove this methodology.

Property Valuation in Latvia and Brazil: A Multifaceted Approach Integrating Algorithm, Geographic Information System, Fuzzy Logic, and Civil Engineering Insights

This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth.

X-RMTV: An Integrated Approach for Requirement Modeling, Traceability Management, and Verification in MBSE

Formal requirements modeling and traceability management are essential for effectively implementing Model-Based Systems Engineering (MBSE). However, few studies have explored the integration of requirement modeling, traceability management, and verification within MBSE-based systems engineering methodologies. Moreover, the predominant modeling language for MBSE, SysML, lacks sufficient capabilities for requirement description and traceability management and for depicting physical attributes and executable capabilities, making it challenging to verify functional and non-functional requirements collaboratively. This paper proposes an integrated approach for requirement modeling, traceability management, and verification, building on the previously proposed integrated modeling and the simulation language called X language. Our contributions primarily include defining the ReqXL specification for MBSE-oriented requirement modeling based on X language, proposing an algorithm for automatically generating requirement traces, and an integrated framework for requirements modeling, traceability management, and verification was developed by combining the X language with ReqXL. These functionalities were customized on the self-developed integrated modeling and simulation platform, XLab, which is specifically tailored for the X language. Furthermore, we showcase the efficacy and promise of our approach through a case study involving the design of an aircraft electrical system.

Explainability of convolutional neural networks for damage diagnosis using transmissibility functions

Structural health monitoring (SHM) is crucial for ensuring the safety and efficiency of engineering systems. Vibration-based signals have been widely employed in numerous studies for damage detection, localization and quantification, thanks to their high sensitivity to structural damages and versatile applicability across various structures. Among these, transmissibility functions (TFs) have emerged as particularly promising because they can be derived from output-only measurements, making them practical for real-world applications. Vibration-based features, including those captured by TFs, provide a wealth of damage-related information, but extracting these features can be challenging with traditional methods and often requires a priori knowledge. To address this challenge, deep learning has gained significant attention for its capacity to handle intricate patterns and extract hidden features. However, deep learning algorithms are often perceived as black-box models due to their complex and heterogeneous layers, which hinder transparency and raise concerns, especially in critical domains. Although numerous studies have highlighted the diagnostic potential of deep learning in SHM with vibration-based data, there has been limited exploration of the rationale behind using deep neural networks (DNNs) for processing TFs. This paper addresses this gap by introducing explainable artificial intelligence (XAI) methods in two distinct TF-based damage diagnostic case studies: a numerical aluminium structural beam and a large-scale experimental steel frame. In the former, damage is simulated as local stiffness reductions, while in the latter, damage is introduced by loosening joint bolts. In both cases, a convolutional neural network (CNN) is used to process raw TF data for damage detection, localization, and quantification in the beam, and for detection and localization in the frame. Subsequently, an XAI method based on the layer-wise relevance propagation (LRP) algorithm is employed to identify the most relevant input features. A pattern analysis is presented to explain recurrent patterns, and a comparison with other XAI methods is proposed for further validation. The results demonstrate that the CNN provides accurate predictions for both case studies, with its focus on damage-sensitive features aligning closely with findings in the existing literature.

Modelling pressure dynamics in a gas pipeline with an in-line sampling

Gas pipelines, whether main, field, or urban, often operate in non-stationary modes. Changes in the operating modes of pumping stations, equipment start-up and shutdown, an in-line sampling or pumping and various factors are causes of instability of pressure, velocity, gas flow rate. Main pipelines are complex engineering systems with the pipeline itself being the main element. Fail-safety is the major factor in the operation of this system. Ensuring operational safety requires studying the movement regimes of the transported medium, particularly study of the dynamics of pressure duringstart-up or shutdown and at specific extraction points. This article aims to build a mathematical model and study the pressure dynamics in a gas pipeline with a sampling point. The theory of non-stationary motion liquid in round pipes has been strongly developed in the works of I. A. Charnyj. These works consider a large complex of engineering tasks taking into account viscous properties of the transported medium and pipe resistance in the hydraulic approximation. In the article on the basis of I. A. Charnyj's researches on the motion of real liquid in circular pipes the equation in partial derivatives of hyperbolic type is compiled. The equation describes the unsteady pressure of a horizontal section of gas pipeline with an extraction point. Using the Dirac delta function allows the formulation of the problem in the form of a single equation. Pressures are set at the ends of a given section, and the initial velocity is related to the Dirac delta function. By applying the finite Fourier sine transform, the partial differential equation is transformed into an ordinary differential equation and solved. Solution of equation is vision of a solution to the initial task. Inverse transform formulas based on Fourier theory allowed us to proceed to the solution of this task. Explicit dependences for the dynamics of unsteady pressure are obtained. The qualitative analysis of the formulas indicates the wave motion of a medium during the initial phase of operation, transitioning into a stationary mode after a brief period. The duration of the transition period depends on factors such as the hydraulic resistance coefficient and the velocity of the transported medium. Coefficient of hydraulic resistance and the velocity of the transported medium are the main factors. An example is considered for a horizontal section under isothermal flow conditions. With assumed numerical parameters, the transition to a stationary state occurs approximately 17 minutes after the process begins, as illustrated in the graphs provided in the article. Engineers can use mathematical models of the liquid and gas motion through pipes in the design of pipelines, as well as in solving tasks that arise during their operation. These tasks include monitoring the condition of the pipeline system, optimizing the operation, accumulation capacity estimates and others.

Genotype from Phenotype: Using CRISPR Screens to Dissect Lymphoma Biology.

Genome-wide screens are a powerful technique to dissect the complex network of genes regulating diverse cellular phenotypes. The recent adaptation of the CRISPR-Cas9 system for genome engineering has revolutionized functional genomic screening. Here, we present protocols used to introduce Cas9 into human lymphoma cell lines, produce high-titer lentivirus of a genome-wide sgRNA library, transduce and culture cells during the screen, select cells with a specified phenotype, isolate genomic DNA, and prepare a custom library for next-generation sequencing. These protocols were tailored for loss-of-function CRISPR screens in human B-cell lymphoma cell lines but are highly amenable for other experimental purposes.

Performance analysis of ER fluid-lubricated two-lobe hole-entry hybrid journal bearings with bionic and spherical texture surfaces optimized by genetic algorithm

This study is done into the multifaceted world of ER (Electro-rheological) fluid-lubricated optimized textured hybrid hole-entry journal bearings to improve dynamic performance of the bearing and stability. These innovative techniques harness the dynamic properties of ER fluids in conjunction with optimized textured spherical and bionic textured surfaces to achieve enhanced performance and reliability. This study aims to explore the influence of ER fluid on the tribological behaviour and controllability of hybrid hole-entry journal bearings. Further it proposes the optimized design of bionic and spherical textured surfaces, capitalizing on their ability to influence fluid flow, load-carrying capacity, and friction reduction. The results demonstrate promising improvements in bearing performance metrics, including dynamic stability and fluid film damping, load-carrying capacity, friction reduction, and controllability, facilitated by the integration of ER fluid and the optimization of textured spherical and bionic textured surfaces. This study offers valuable insights into the potential applications of ER fluids and biomimicry-inspired textured surfaces in bearing system engineering. The inclusion of bionic textured hole-entry hybrid bearing lubricated with ER fluid improves the stability by up to 112.57 % and decrease the friction coefficient up to 23.37 %. The findings are expected to inform the design and development of high-performance hole-entry hybrid journal bearings with diverse industrial applications.

Anaerobic Degradation of Aromatic and Aliphatic Biodegradable Plastics: Potential Mechanisms and Pathways.

Biodegradable plastics (BDPs) have been widely used as substitutes for traditional plastics, and their environmental fate is a subject of intense research interest. Compared with the aerobic degradation of BDPs, their biodegradability under anaerobic conditions in environmental engineering systems remains poorly understood. This study aimed to investigate the degradability of BDPs composed of poly(butylene adipate-co-terephthalate) (PBAT), poly(lactide acid) (PLA), and their blends, and explore the mechanism underlying their microbial degradation under conditions of anaerobic digestion (AD). The BDPs readily depolymerized under thermophilic conditions but were hydrolyzed at a slow rate under conditions of mesophilic AD. After 45 days of thermophilic AD, a decrease in the molecular weight and significant increase in the production of methane and carbon dioxide production were observed. Network and metagenomics analyses identified AD as reservoirs of plastic-degrading bacteria that produce multiple plastic-degrading enzymes. PETase was identified as the most abundant plastic-degrading enzyme. A potential pathway for the anaerobic biodegradation of BDPs was proposed herein. The polymers of high molecular weight were subjected to abiotic hydrolysis to form oligomers and monomers, enabling subsequent microbial hydrolysis and acetogenesis. Ultimately, complete degradation was achieved predominantly via the pathway involved in the conversion of acetic acid to methane. These findings provide novel insight into the mechanism underlying the anaerobic degradation of BDPs and the microbial resources crucial for the efficient degradation of BDPs.

Grey Bayesian network model for electromagnetic pulse vulnerability assessment of engine system

Firstly, in view of the uncertainty of electromagnetic pulse vulnerability assessment of engine system caused by limited experimental data and incomplete information, a grey Bayesian network model is proposed by introducing interval grey numbers in grey system theory to characterize the uncertainty of sensitivity threshold and fault logic relationship of engine system components, so as to improve the processing ability of Bayesian network for uncertain information. Secondly, taking broadband high-power microwave as an example, the interval grey number failure probability of engine system and the posterior failure probability of sensor interval grey number are calculated. The former represents the survivability of the engine system as a whole under strong electromagnetic pulse, and the latter reflects the vulnerability order of each sensor under the condition of engine system failure. The evaluation conclusion can provide a reference for the design of vehicle electromagnetic protection.

Inverse-designed 3D sequential metamaterials achieving extreme stiffness

Mechanical metamaterials signify a groundbreaking leap in material science and engineering. The intricate and experience-dependent design process poses a challenge in uncovering architectural material sequences with exceptional mechanical properties. This study introduces inverse-designed 3D sequential metamaterials with outstanding mechanical attributes, achieved through a novel computational framework. The explored sequences based on Schoen's I-graph–wrapped package (IWP) and Schwarz Primitive (Schwarz P) surpass the Hashin-Shtrikman upper bound of Young's modulus at relative densities of 0.24 and 0.43, outperforming previous records. Optimized Body-Centered-Cubic (BCC) truss-based sets outperform traditional ones by 72.7%. This innovative approach can be extended for metamaterial customization, involving the optimization of multi-directional Young's modulus, total stiffness, and the addition of isotropy constraints. The paper explores the characteristics and implications of this innovation, emphasizing the impact of geometric and topological variations on mechanical performance. These metamaterial sequences offer unparalleled adaptability, and hold significant potential in structural engineering and adaptive mechanical systems, opening avenues for technological advancements.

A comparative study of finite difference approach and bvp4c techniques for water base hybrid nanofluid containing multiple walls carbon nanotubes and magnetic oxide

This study investigates the thermal behaviour of unsteady hybrid nanofluid flow on an infinite vertical plate. The investigation takes into account parameters such as magnetohydrodynamics and radiation effects, as well as the stratified medium. The systems of equations were solved by employing the explicit finite difference approach of Dufort-Frankel method. The main motivation of the study is to compare the performance of water, magnetic oxide, and multi-wall carbon nanotubes as working fluids. Additionally, velocity, temperature, and concentration outlines are visualized through plots, elucidating the fluid behaviour. Tables are provided for the Skin friction, Nusselt number, and Sherwood number, offering comprehensive insights crucial for optimizing performance in engineering applications ranging from thermal management systems to renewable energy technologies. The main finding of this study indicates that the quantitative result reveals that the temperature outline escalates among increasing values of radiation. In contrast, the outlines of a velocity and concentration show a decrease as the values of magnetohydrodynamics increase. In addition, multi-walled carbon nanotubes consume a larger outcome on temperature. A statistical study displays that the thermal stream rate of magnetic oxide-multi-wall carbon nanotubes-water increases from 1.7615 percentages to 7.4415 percentages, respectively, when the volume fraction of nanoparticles rises from 0.01 to 0.05. Future research is important to understanding hybrid nanofluid flows and their applications in thermal engineering systems such as energy systems, nuclear reactors, biomedical applications, electronics cooling, solar thermal systems, chemical processing, and other heat transfer applications where improved thermal performance is crucial.

Editorial Preview

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This particular October 2024 release contains 12 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Software Engineering, Recommender Systems, Cybersecurity, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 7 papers curated by our Thematic Editor, Dr. Ji-Jian Chin, under the theme "Pervasive Computing." In his editorial, Dr. Ji-Jian Chin highlights cutting-edge research on the integration of computing into everyday environments. Moreover, these papers are aligned to some of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) through advancements in healthcare technologies, SDG 9 (Industry, Innovation, and Infrastructure) through software and systems innovation research, and SDG 16 (Peace, Justice, and Strong Institutions) through contributions to privacy and cybersecurity research.

Metal Cocatalyst Engineering in Metal-Semiconductor Hybrid Photocatalysts Achieves a Fivefold Enhancement of Hydrogen Evolution.

This study explores the optimal morphology of photochemical hydrogen evolution catalysts in a one-dimensional system. Systematic engineering of metal tips on precisely defined CdSe@CdS dot-in-rods is conducted to exert control over morphology, composition, and both factors. The outcome yields an optimized configuration, a Au-Pt core-shell structure with a rough Pt surface (Au@r-Pt), which exhibits a remarkable fivefold increase in quantum efficiency, reaching 86 % at 455 nm and superior hydrogen evolution rates under visible and AM1.5 G irradiation conditions with prolonged stability. Kinetic investigations using photoelectrochemical and time-resolved measurements demonstrate a greater extent and extended lifetime of the charge-separated state on the tips as well as rapid water reduction kinetics on high-energy surfaces. This approach sheds light on the critical role of cocatalysts in hybrid photocatalytic systems for achieving high performance.

Cyber Resilience Limitations in Space Systems Design Process: Insights from Space Designers

Space technology is integral to modern critical systems, including navigation, communication, weather, financial services, and defence. Despite its significance, space infrastructure faces unique cyber resilience challenges exacerbated by the size, isolation, cost, persistence of legacy systems, and lack of comprehensive cyber resilience engineering standards. This paper examines the engineering challenges associated with incorporating cyber resilience into space design, drawing on insights and experiences from industry experts. Through qualitative interviews with engineers, cybersecurity specialists, project managers, and testers, we identified key themes in engineering methodologies, cybersecurity awareness, and the challenges of integrating cyber resilience into space projects. Participants emphasised the importance of incorporating cybersecurity considerations from the earliest stages of design, advocating for principles such as zero-trust architecture and security by design. Our findings reveal that experts favour Model-Based Systems Engineering (MBSE) and Agile methodologies, highlighting their synergy in developing flexible and resilient systems. The study also underscores the tension between principles-based standards, which offer flexibility but can lead to inconsistent implementation, and compliance-based approaches, which provide clear measures but may struggle to adapt to evolving threats. Additionally, the research recognises significant barriers to achieving cyber resilience, including insider threats, the complexity of testing and validation, and budget constraints. Effective stakeholder engagement and innovative funding models are crucial for fostering a culture of cybersecurity awareness and investment in necessary technologies. This study highlights the need for a comprehensive cyber resilience framework that integrates diverse engineering methodologies and proactive security measures, ensuring the resilience of space infrastructure against emerging cyber threats.

Rethinking Loss of Available Work and Gouy-Stodola Theorem

Abstract Exergy represents the maximum useful work possible when a system at a specific state reaches equilibrium with the environmental dead state at temperature To. Correspondingly, the exergy difference between two states is the maximum work output when the system changes from one state to the other, assuming that during the processes, the system exchanges heat reversibly with the environment. If the process involves irreversibility, the Guoy-Stodola theorem states that the exergy destruction equals the entropy generated during the process multiplied by To. The exergy concept and the Gouy-Stodola theorem are often used to optimize processes or systems, even when they are not directly connected to the environment. In the past, questions have been raised on if To is the proper temperature to use in calculating the exergy destruction. Here, we start from the first and the second laws of thermodynamics to unambiguously show that the useful energy loss (UEL) of a system or process should equal to the entropy generation multiplied by an equivalent temperature associated with the entropy rejected out of the entire system. For many engineering systems and processes, this entropy rejection temperature can be easily calculated as the ratio of the changes of the enthalpy and entropy of the fluid stream carrying the entropy out, which we call the state-change temperature. The UEL is unambiguous and independent of the environmental dead state, and it should be used for system optimization rather than the exergy destruction.

Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach

Data-driven models have become powerful tools for structural and condition monitoring of engineering systems, particularly wind turbines. This paper presents a comparative analysis of common machine learning (ML) algorithms (artificial neural networks, linear regression, random forests, and gradient boosting) and a cointegration-based approach for fault detection using Supervisory Control and Data Acquisition (SCADA) data. While ML models offer early fault prediction, the cointegration method is simpler, requires less training data, and has lower computational costs. However, it is less effective for early detection. To balance these trade-offs, we propose a cascading monitoring framework, where the ML model provides long-term predictions (outer monitoring process) and the cointegration model offers short-term verification (inner monitoring process). The cointegration model serves to confirm anomalies flagged by the ML model. By combining both models in a cascade structure, the system reduces the risk of false alarms triggered by uncertainties in the ML model alone. Furthermore, the short-term cointegration-based prediction model helps pinpoint immediate risks and mitigate the issue of prolonged downtime. This combination enhances both accuracy and reliability, as demonstrated through testing on a five-year SCADA dataset from a commercial wind turbine with a known gearbox fault.

Thermal spreading in a multilayer geometry with convective cooling along the side wall of each layer

Two-dimensional thermal conduction between a source/sink and a body of larger size results in thermal spreading and constriction, which are of much technological importance in problems such as semiconductor thermal management. While the traditional analysis of thermal spreading and constriction assumed adiabatic side walls, there is growing interest in convective heat removal from the side walls, such as in multilayer three-dimensional integrated circuits (3D ICs) and stacked Li-ion cells in a battery pack. This work presents theoretical analysis of thermal spreading in a multilayer body with distinct convective heat transfer coefficients along the side wall for each layer, in addition to anisotropic thermal conductivity in each layer and thermal contact resistance at interfaces between layers. A series solution for the temperature distributions in each layer is derived, with a unique set of eigenvalues for each layer. A set of linear algebraic equations that govern the coefficients of the series solutions is derived. Results are shown to agree well with past work for special cases, as well as with numerical simulations for general problems. The impact of source size, thermal anisotropy and Biot numbers of various layers is analyzed in detail. In particular, the interplay between Biot numbers along the sidewall and at the sink plane in determining total thermal resistance is investigated in detail. This work contributes towards a fundamental understanding of theoretical thermal conduction in problems of practical relevance, such as advanced microelectronics and Li-ion cells. Results may find application in the design and optimization of these and other engineering systems where thermal spreading or constriction play an important role.

Shape-Morphing in Oxide Ceramic Kirigami Nanomembranes.

Interfacial strain engineering in ferroic nanomembranes can broaden the scope of ferroic nanomembrane assembly as well as facilitate the engineering of multiferroic-based devices with enhanced functionalities. Geometrical engineering in these material systems enables the realization of 3-D architectures with unconventional physical properties. Here, 3-D multiferroic architectures are introduced by incorporating barium titanate (BaTiO3, BTO) and cobalt ferrite (CoFe2O4, CFO) bilayer nanomembranes. Using photolithography and substrate etching techniques, complex 3-D microarchitectures including helices, arcs, and kirigami-inspired frames are developed. These 3-D architectures exhibit remarkable mechanical deformation capabilities, which can be attributed to the superelastic behavior of the membranes and geometric configurations. It is also demonstrated that dynamic shape reconfiguration of these nanomembrane architectures under electron beam exposure showcases their potential as electrically actuated microgrippers and for other micromechanical applications. This research highlights the versatility and promise of multi-dimensional ferroic nanomembrane architectures in the fields of micro actuation, soft robotics, and adaptive structures, paving the way for incorporating these architectures into stimulus-responsive materials and devices.

Electrically Conductive Nanomaterials: Transformative Applications in Biomedical Engineering - A Review.

In recent years, significant advancements in nanotechnology have improved the various disciplines of scientific fields. Nanomaterials, like, carbon-based (carbon nanotubes, graphene), metallic, metal oxides, conductive polymers, and 2D materials (MXenes) exhibit exceptional electrical conductivity, mechanical strength, flexibility, thermal property and chemical stability. These materials hold significant capability in transforming material science and biomedical engineering by enabling the creation of more efficient, miniaturized, and versatile devices. The indulgence of nanotechnology with conductive materials in biological fields promises a transformative innovation across various industries, from bioelectronics to environmental regulations. The conductivity of nanomaterials with a suitable size and shape exhibits unique characteristics, which provides a platform for realization in bioelectronics as biosensors, tissue engineering, wound healing, and drug delivery systems. It can be explored for state-of-the-art cardiac, skeletal, nerve, and bone scaffold fabrication while highlighting their proof-of-concept in the development of biosensing probes and medical imaging. This review paper highlights the significance and application of the conductive nanomaterials associated with conductivity and their contribution towards a new perspective in improving the healthcare system globally.&#xD.

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.