engineering design research paper

Research in Engineering Design

• Focuses on mechanical, civil, architectural, and manufacturing engineering.
• Emphasizes the underlying principles of engineering design.
• Examines theories of design, foundations of design environments, and models of design processes.
• Aims to integrate design and manufacturing.
• Publishes state-of-the-art review articles.
• Yoram Reich

Latest issue

Volume 35, Issue 2

Latest articles

Fostering self-efficacy through usability and emotional product design an explorative study.

• Stefan T. Kamin
• Jörg Miehling

Evaluating the effectiveness of functional decomposition in early-stage design: development and application of problem space exploration metrics

• Jinjuan She
• Elise Belanger
• Caroline Bartels

Mapping the landscape of product models in embodiment design

• Lukas Paehler
• Sven Matthiesen

Module partition for complex products based on stable overlapping community detection and overlapping component allocation

• Pengcheng Zhong
• Jianrong Tan

Optimize or satisfice in engineering design?

• Janet K. Allen
• Farrokh Mistree

Journal updates

Request for proposals for editor-in-chief research in engineering design.

We are announcing an open call for proposals for the position of Editor-in-Chief for our esteemed journal, Research in Engineering Design. As a leading publication in the field, we are seeking an experienced and visionary individual to lead the editorial team and further elevate the journal's impact in the academic community.

Journal information

• Current Contents/Engineering, Computing and Technology
• EI Compendex
• Google Scholar
• INIS Atomindex
• Japanese Science and Technology Agency (JST)
• OCLC WorldCat Discovery Service
• Science Citation Index Expanded (SCIE)
• TD Net Discovery Service
• UGC-CARE List (India)

Rights and permissions

Springer policies

© Springer-Verlag London Ltd., part of Springer Nature

• Find a journal
• Publish with us
• Track your research

• Next Article

Artificial Intelligence and Engineering Design

Engineering design research themes, ai method themes, future opportunities for ai and engineering design research, special issue: artificial intelligence and engineering design.

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Open the PDF for in another window
• Cite Icon Cite
• Permissions
• Search Site

Allison, J. T., Cardin, M., McComb, C., Ren, M. Y., Selva, D., Tucker, C., Witherell, P., and Zhao, Y. F. (January 11, 2022). "Special Issue: Artificial Intelligence and Engineering Design." ASME. J. Mech. Des . February 2022; 144(2): 020301. https://doi.org/10.1115/1.4053111

Download citation file:

• Ris (Zotero)
• Reference Manager

Artificial intelligence (AI) has had a strong presence in engineering design for decades, and while theory, methods, and tools for engineering design have advanced significantly during this time, many grand challenges remain. Modern advancements in AI, including new strategies for capturing, storing, and analyzing data, have the potential to revolutionize engineering design processes in a variety of ways. The purpose of this special issue is to consolidate recent research activities that utilize existing or new AI methods to advance engineering design knowledge and capabilities.

During the conception of this special issue, we identified three core interfaces between the research domains of engineering design and AI: (1) leveraging AI methods directly in engineering design methods, (2) creating new AI capabilities that are inspired by unique challenges that arise in engineering design, and (3) creating and analyzing design methods that are tailored for the design of engineering systems where the systems themselves utilize AI, such as autonomous vehicles. The diverse body of research articles that now comprise this special issue gravitate toward the first of these themes: advancing engineering design capability through the use of AI. While these articles are an exciting contribution to the design research literature, significant opportunities exist for more fully exploring the remaining two interfaces, ideally through more unified interdisciplinary efforts. During the process of synthesizing this editorial, we recognized a fourth interface between engineering design and AI: specifically, investigating how AI could be used as an increasingly powerful tool for conducting engineering design research, such as AI tools that are used directly in research activities (e.g., experiment planning or gathering information from human designers) and that are not necessarily part of the designed system or the design method.

Two sets of clear themes have emerged from this special issue. The first set is expressed from the perspective of engineering design research and design processes. The second set is organized in terms of AI methods. We have organized these themes in this way in part to facilitate clearer communication across AI and Engineering Design research communities and to enable the productive collaboration that is needed to address open questions. Later, we discuss the relationship between AI and engineering design and then articulate the two sets of themes found in this special issue. Finally, a vision is presented for advancing interdisciplinary research in this area, including an initial outline of promising research topics.

This special issue consolidates exciting new outcomes from recent research that focused on the application of modern AI methods to the creation and analysis of methods that advance engineering design capabilities. Design researchers have long recognized that AI tools can be used in many ways to advance how engineers perform design. For example, designers can now leverage AI to support more automatic and intelligent knowledge extraction and knowledge representation, support early design ideation, and even to discover design solutions to previously unsolved engineering problems. In addition, the capabilities of modern AI algorithms can enhance the efficacy of later-stage system design activities, such as those involving high-dimension and strongly interrelated detailed design decisions.

Tasks at early design stages, such as concept generation, have the potential to effect transformational change upon engineering system capabilities. Yet in practice, these early-stage tasks rely heavily upon designer experience and intuition. Human cognitive limits constrain such design processes, creating an opportunity for AI models to accelerate the ability of engineering to better meet the needs of humanity. Limitations of conventional methods and tools constrain the fidelity and scope of the design spaces that are tractable. AI methods are helping to solve problems where human intuition or human processing capabilities are insufficient, making it possible to expand the richness of design spaces that can be navigated successfully through AI support.

Some engineering functions may not even have known solutions, even via biological analogies; AI-based design strategies have the potential to aid in the discovery of design solutions to previously unrealized functions through nonobvious design configurations. Used as an identification tool, AI is enabling more efficient and higher-performance design solutions during design revision at the manufacturing and assembly level by screening and identifying part consolidation opportunities and multifunctional design potential.

More comprehensive investigation of the role of AI in engineering design methods and research appears to be reshaping how we think about design. AI methods that can transformatively restructure design spaces for various goals exist, such as more efficient navigation of design alternatives or insightful interrogation to extract design knowledge. Design spaces can be adaptable throughout design process stages. Less rigid formulations can produce more fluid design paradigms compared to established frameworks, such as design optimization. AI models can also serve as the design solution method without needing to be linked to iterative design optimization. Moreover, inverse design approaches fit well with AI tools, further bending our conception of design strategies. Publications at the interface of AI and engineering design have often been led by design research experts rather than AI experts, resulting in potential bias toward particular research questions and classes of AI methods. Approaching this interface from additional complementary directions could help us build a more complete understanding of how AI methods can further benefit society through advancements in engineering design.

Articles in this issue link to a diverse set of approaches used in AI and utilize various neural network architectures to develop surrogate models. These surrogate models typically learn abstract representations to understand the underlying processes, which can be then explained to gain further insight. Several approaches also assist the designer through human augmentation and increased automation, while also expanding design spaces through transfer learning. These approaches are applied to many aspects of the design process including systems design, conceptual design, generative design, and several detailed design applications in areas including supply chains, medical devices, and aeronautics.

Readers may be keenly aware that the guest editors have not attempted to define AI. While some excellent definitions exist, conflicts in (sometimes evolving) AI definitions held by such a diverse set of potential authors are likely. We opted to consider submissions that utilized AI as defined by the authors, as long as the definition is justifiable, and if the submission makes a notable contribution to the body of engineering design knowledge. As a result of this inclusive approach, some readers may not consider some methods appearing in this issue to be AI. The definition of AI will not be settled here; rather, we hope that one of the primary contributions of this special issue is a helpful step toward more successful integration of AI and engineering design research.

This special issue incorporates a variety of topics that represent the state of the art in how AI is changing how we do engineering design today. Articles solicited represent intellectual contributions to engineering design research at the intersection of unmet engineering challenges, advances in AI, and design-relevant contexts. Articles published in this issue fall largely within the general area of AI being utilized as an enabling tool for advancing engineering design capabilities. In particular, many articles focus on enhancing early-stage design capabilities. Previous special issues exist for related topics, including Machine Learning for Engineering Design and Data-Driven Design . This special issue has provided an opportunity to publish design research linking to the broad area of AI that may not have fit well with these previous special issues. For example, one article (Gyory et al.) utilizes an AI agent to perform dynamic design team management based on predefined logical rules.

The guest editors have identified significant themes that have emerged from this collection of articles, organized first into topics that are presented from an engineering design research perspective and second into topics that are articulated from an AI method perspective. Please note that the distinctions between these themes can be fuzzy; some studies lie at the intersection between two or more themes. The following two sections introduce these themes and point readers to relevant articles in this special issue. This editorial then concludes with remarks on open research questions at the interface of AI and engineering design.

When viewed through the lens of engineering design research, four broad themes emerge from this special issue, each of which is discussed here.

Design Theme 1: Conceptual Design and Design Synthesis. Several articles involve advances in utilizing AI for more expansive and comprehensive yet practical exploration of complex design configuration spaces, including topological changes, design synthesis, and support for enhanced human creativity. Methods are either automated or involve a partnership between AI and human designers. More conventional design paradigms induce a different constraining of design spaces than might be possible with this class of AI-based design methods. Natural language processing (NLP) is utilized in several studies presented here as a key interface with the human creative process (T. Chen et al., Han et al., and Lee et al.). Two survey papers also provide context and future directions for utilizing the AI-based methods for this and other design needs (Han et al. and Jiang et al.). The article by Song et al. presents a study of the influence that AI tools have on strategies followed by designers during engineering design processes. As noted earlier, several articles link to two or more themes. For example, Behzadi et al., Q. Chen et al., Nobari et al., Quigley et al., and Raina et al. all link to both methods for conceptual design and Design Theme 2: Accelerating Design Processes.

Design Theme 2: Accelerating Design Processes. Multiple contributions in this special issue utilize AI tools to reduce the time, computational iterations, or other resources required to obtain a good design solution. Two key AI-based strategies employed to accelerate iterative design processes include (1) transfer and representation learning (Behzadi et al., Herzog et al., and Whalen et al.) and (2) invertible neural networks for optimization (Ghosh et al. and Oddiraju et al.). While many current AI-related research efforts outside of engineering design research focus on training models from scratch for maximum accuracy with large datasets, many articles in this special issue (and other design research publications) gravitate toward pretrained networks, such as used in transfer learning, to enhance accuracy with limited datasets (Ferrero et al. and Yuan et al.). This observation links to the Transfer Learning AI theme discussed later. In addition, AI methods that better support generation of design solutions that are substantially distinct from previous designs, such as transfer learning methods that are successful in design domains that are more distant from the training data, may be particularly valuable.

Design Theme 3: AI-Based Direct Estimation and Tuning. In addition to accelerating iterative design processes, some methods proposed AI-based tools that reduce or eliminate the need for iterative design. Articles by Burge et al., Dachowicz et al., and Li et al. used neural networks to design solutions for given initial conditions. The article by Raina et al. used learning by demonstration to provide design solutions, and the article by Caputo et al. utilized deep reinforcement learning to achieve adaptive responses to changing environmental conditions without the need for any new design iterations.

Design Theme 4: Broader Design Process Support. A few articles make broader contributions that cannot be strictly classified along a specific theme mentioned earlier. The article by Wang et al. makes a theoretical contribution involving the use of Gaussian processes in a scalable manner to represent complex spaces. The article by Kim et al. utilizes AI-based tools to perform sentiment analysis on product listings and how they evolve. The article by Gyory et al. proposes and tests an AI-based team management system against a human-based management system; an overall increase in productivity and content is realized through the AI system. The article by Caputo et al. proposes a new approach to conceptualize real options and flexibility analysis in engineering systems design, building upon principles from deep reinforcement learning. This supports the view that more work is needed to develop new AI and data-driven methods to design complex engineered systems so that they can better deal with uncertainty and risks—which is much needed, especially given ongoing threats from climate change and global healthcare emergencies.

When viewed from the perspective of identifying specific AI methods that are shown to be useful for advancing engineering design capabilities, five themes materialize. An exposition of these themes is provided below.

AI Theme 1: Natural Language Processing. Natural language processing has been used in several papers included in this special issue. NLP serves as a human-interpretable way to interact with many AI-based systems, and so improvements to NLP methods can benefit the tools adapted to engineering design problems (T. Chen et al., Han et al., Kim et al., Lee et al., and Yuan et al.).

AI Theme 2: Graph (Neural) Networks . Many design representations may be represented as graphs (e.g., assemblies (Ferrero et al.) and structures (Whalen et al.)). In future work, it would be beneficial to see more studies that address remaining challenges in applying graph networks to engineering design, such as invariance/equivariance properties, explainability, and transferability.

AI Theme 3: Generative Models. Use of generative models continues as a demonstrated AI-based design strategy (Behzadi et al., Q. Chen et al., Nobari et al., Oddiraju et al., Quigley et al., Yuan et al.); one potential benefit is the ability for these models to produce solutions without iterative optimization. Within the set of articles utilizing generative models, articles by Behzadi et al. and Nobari et al. address the challenges with small data set sizes, which is an important characteristic of many data-driven design strategies. To enhance what can be done using small data sets, the article by Nobari et al. introduces a label-aware self-augmentation training approach, and the article by Behzadi et al. uses knowledge transfer.

AI Theme 4: Transfer Learning . Transfer learning is emerging as an important tool for dealing effectively with the small data sets that are common in engineering design. The article by Whalen et al. explores the transferability of a learned graph network on a variety of structural design problems. The article by Herzog et al. presents a study of the effectiveness of knowledge transfer in the context of semi-supervised learning of labels on 3D geometries.

AI Theme 5: Representation Learning. The article by Raina et al. introduces new ways of representing design policies (e.g., sequences of design decisions) when the action space structurally changes during the design process. Gaussian process (GP) models that learn latent categorical representations are presented in the article by Wang et al., and the scalability issue of GP models is investigated.

This special issue offers a partial snapshot of the current research results in AI and engineering design. The previous sections summarize some aspects that were addressed in the special issue; here, we articulate what appears to be missing, and what could serve as the basis for rich future collaborations in interdisciplinary research between AI and engineering design researchers. We identify both AI methods that need further study as elements in engineering design methods, as well as ways AI could be addressed differently in the context of engineering design (e.g., new AI contributions motivated by design, design of AI-based systems, and AI methods as tools in engineering design research).

The collection of articles utilizes several well-established model-free AI methods for the purpose of accelerating engineering design processes, but leaves open the opportunity to investigate the use of model-based learning techniques in these contexts. This may be because most of the applications studied here do not have clear associated dynamics. The solutions obtained from these methods can be used not only as solutions to engineering design problems but also model-based strategies that leverage assumptions about the form of the system being modeled (e.g., physics-based phenomena or distributions) could lead to a deeper qualitative design understanding. Model- and dynamics-based learning methods could be exciting and insightful avenues for engineering design research.

Several articles in this issue utilize pretrained networks, which are either adapted to identify/classify design models or to develop a framework for explaining engineering design related processes. While the pretraining approach has been discussed in AI under the names of transfer and curriculum learning, its deeper understanding is particularly important to engineering design due to frequent scenarios where data are limited or expensive, e.g., during the exploration of new material systems. In such scenarios, it would be valuable to be able to learn effective representations, rules, or solution search strategies from one set of design problems and apply that knowledge to a different (or more complicated) set of problems. While empirical evidence has been reported where such transfer has potential, we expect deeper investigation into the theoretical conditions for certifiable performance of pretrained models, and more comprehensive ontologies that define knowledge transfer within the broad context of engineering design. The tendency toward pretraining approaches in engineering design methods exemplifies the deeper underlying needs that are unique to engineering design and motivate additional fundamental AI investigations. This observation is representative of the second interface between AI and engineering design research, as identified in the second paragraph of this editorial, and is an opportunity for productive interdisciplinary discussion.

An interesting future direction for this effort would be to solve engineering design problems using both the pretraining and from scratch strategies for neural networks and then to compare the abstractions learned using both approaches. It would also be valuable to evaluate hybrid methods, where certain layers are allowed to mutate to adapt to the given problem. Alternately, networks trained from one paradigm can be used as the initialization for the other, and optimal abstractions can be learned for the given problem sets. As observed earlier, many articles in this issue concentrated on new AI-enabled design methods to better support early-stage design tasks. Research topics that are notably absent from a design methods research perspective include those that investigate AI-based methods intended for more comprehensive late-stage design (e.g., AI agents that aid automation of fully detailed design specifications required for the production).

In addition to the outputs of AI research benefitting design research, the converse is also possible. For example, tools from current engineering design research, such as design automation methods, could be used to rapidly generate rich and tailored data sets that amplify the impact of AI methods. Furthermore, engineering design challenges and knowledge from design research could help inform new advancements in AI, presenting a new opportunity for interdisciplinary collaboration and motivation for enhanced communication across research disciplines. Another class of open research questions involves the creation of design methods that are tailored to meet the needs that are unique to design systems where AI is part of the system being designed and not necessarily part of the design method. Finally, AI methods could be used as a tool in engineering design research, which is distinct from use as part of a design method. For example, many design research studies involve human components; AI could play a significant role as a research tool in interfacing with human designers, users of designed systems, and other stakeholders. It could also transform planning of research experiments and data collection.

Addressing these questions is distinct from fundamental advances in AI theory and algorithms alone; generation of new knowledge that is relevant to engineering design and that links to AI requires scientific rigor in both domains and motivates deeper collaboration between design research and AI research experts. Completely new AI concepts may take engineering design capabilities in unexpected directions, and vice versa. Many important open questions remain, and only a few have been highlighted here. We encourage readers to apply their own unique perspective and expertise to distill and identify additional scientific questions at the intersection of AI and engineering design research, especially those that may benefit from deep interdisciplinary collaboration. We hope to work collectively toward more impactful research at the interface of AI and engineering design. Achieving this will require extensive future efforts. Existing work does involve independent forays into adjacent disciplines (e.g., design researchers gaining AI expertise), but, on the whole, studies at this interface are still performed in a largely siloed manner. Perhaps the fundamental differences in language, perspectives, and goals between the AI and engineering design research communities add to the underlying difficulties. A significant investment across disciplines would be required to realize this vision of intrinsic interdisciplinarity for research at the interface of AI and engineering design.

Get Email Alerts

Related articles, related proceedings papers, related chapters, affiliations.

• Accepted Manuscripts
• ASME Journal Media
• About the Journal
• Editorial Board
• Information for Authors
• Call for Papers
• Rights and Permission
• Online ISSN 1528-9001
• Print ISSN 1050-0472

ASME Journals

• About ASME Journals
• Submit a Paper
• Title History

ASME Conference Proceedings

• About ASME Conference Publications and Proceedings
• Conference Proceedings Author Guidelines

ASME eBooks

• About ASME eBooks
• ASME Press Advisory & Oversight Committee
• Book Proposal Guidelines
• Frequently Asked Questions
• Publication Permissions & Reprints
• ASME Membership

Opportunities

• Faculty Positions
• ASME Community

• Accessibility
• Privacy Statement
• Terms of Use
• Get Adobe Acrobat Reader

This Feature Is Available To Subscribers Only

Sign In or Create an Account

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals

Mechanical engineering articles from across Nature Portfolio

Mechanical engineering is the branch of engineering that deals with moving machines and their components. A central principle of mechanical engineering is the control of energy: transferring it from one form to another to suit a specific demand. Car engines, for example, convert chemical energy into kinetic energy.

Latest Research and Reviews

An integrated method for compensating and correcting nonlinear error in five-axis machining utilizing cutter contacting point data

• Liangji Chen
• Pengcheng Wang

Nonlinear dynamic separation characteristics of friction pair and experimental analysis

• Yongqiang Zhao
• Huajun Chen
• Xiangdong Ni

Programmable photoacoustic patterning of microparticles in air

Here the authors integrate optical and acoustic manipulation techniques to generate localized Lamb fields that emulate arbitrary laser patterns and demonstrate programmable nanoparticle patterning over a centimeter-scale area.

• Ruoqin Zhang
• Xichuan Zhao

A six degrees-of-freedom cable-driven robotic platform for head–neck movement

• Haohan Zhang

Experimental analysis and safety assessment of thermal runaway behavior in lithium iron phosphate batteries under mechanical abuse

• Zhixiong Chai

Study on monitoring broken rails of heavy haul railway based on ultrasonic guided wave

• Zecheng Pan

News and Comment

Micro- and nanorobots for biofilm eradication

Micro- and nanorobots present a promising approach for navigating within the body and eliminating biofilm infections. Their motion can be remotely controlled by external fields and tracked by clinical imaging. They can mechanically disrupt the biofilm matrix and kill the dormant bacterial cells synergistically, thereby improving the effectiveness of biofilm eradication.

• Staffan Kjelleberg

Mechanism of plastic deformation in metal monochalcogenides

Metal monochalcogenides — a class of van der Waals layered semiconductors — can exhibit ultrahigh plasticity. Investigation of the deformation mechanism reveals that on mechanical loading, these materials undergo local phase transitions that, coupled with the concurrent generation of a microcrack network, give rise to the ultrahigh plasticity.

Adaptable navigation of magnetic microrobots

An article in Nature Machine Intelligence presents an adaptable method to control magnetic microrobots’ navigation using reinforcement learning.

• Charlotte Allard

Soft sensing and haptics for medical procedures

Minimally invasive surgery (MIS) lacks sufficient haptic feedback to the surgeon due to the length and flexibility of surgical tools. This haptic disconnect is exacerbated in robotic-MIS, which utilizes tele-operation to control surgical tools. Tactile sensation in MIS and robotic-MIS can be restored in a safe and conformable manner through soft sensors and soft haptic feedback devices.

• Arincheyan Gerald
• Sheila Russo

Propelling the widespread adoption of large-scale 3D printing

3D printing can be used to automate the manufacturing of building elements for large-scale structures such as skyscrapers, aircraft, rockets and space bases without human intervention. However, challenges in materials, processes, printers and software control must first be overcome for large-scale 3D printing to be adopted for widespread applications.

• Wouter De Corte
• Viktor Mechtcherine

Exploration of truss metamaterials with graph based generative modeling

Optimisation tasks in the inverse design of metamaterials with machine learning were limited due to the representations of generative models. Here the author comments a recent publication in Nature Communications which generates a latent space representation that unlocks non-linear optimisations.

• Angkur Jyoti Dipanka Shaikeea

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

• Science Projects
• Project Guides
• STEM Activities
• Lesson Plans
• Video Lessons

Engineering Design Process

What is the engineering design process.

Many times the solution to a problem involves designing a product (like a machine or computer code) that meets certain criteria and/or accomplishes a certain task. This process is different from the Steps of the Scientific Method , which you may be more familiar with. If your project involves making observations and doing experiments, you should probably follow the Scientific Method. If your project involves designing, building, and testing something, you should probably follow the Engineering Design Process. If you still are not sure which process to follow, you should read Comparing the Engineering Design Process and the Scientific Method .

The engineering design process begins by defining a problem and completing background research on the problem. Requirements are specified and a solution is chosen. A prototype of the solution is built and then tested. If the solution built meets the requirements then the results can be shared. If the solution does not meet all the requirements then another solution is thought of and tested. Each iteration should use the data from the previously tried solution to meet all of the initial requirements.

Engineers do not always follow the engineering design process steps in order, one after another. It is very common to design something, test it, find a problem, and then go back to an earlier step to make a modification or change to your design. This way of working is called iteration , and it is likely that your process will do the same!

Steps of the Engineering Design Process

1. Define the Problem

The engineering design process starts when you ask the following questions about problems that you observe:

• What is the problem or need?
• Who has the problem or need?
• Why is it important to solve?

[Who] need(s) [what] because [why].

• Define the Problem
• Design Notebook
• Mind Mapping

2. Do Background Research

Learn from the experiences of others — this can help you find out about existing solutions to similar problems, and avoid mistakes that were made in the past. So, for an engineering design project, do background research in two major areas:

• Users or customers
• Existing solutions
• Background Research Plan for an Engineering Design Project
• Finding Information
• How to Write a Bibliography in APA and MLA styles With Examples
• Research Paper

3. Specify Requirements

Design requirements state the important characteristics that your solution must meet to succeed. One of the best ways to identify the design requirements for your solution is to analyze the concrete example of a similar, existing product, noting each of its key features.

• Specify Requirements
• Design Requirement Examples
• How to Analyze a Physical Product
• How to Analyze a Software Product or Website
• How to Analyze an Environment
• How to Analyze an Experience
• How Many Design Requirements?

4. Brainstorm Solutions

There are always many good possibilities for solving design problems. If you focus on just one before looking at the alternatives, it is almost certain that you are overlooking a better solution. Good designers try to generate as many possible solutions as they can.

• Brainstorm Multiple Solutions

5. Choose the Best Solution

Look at whether each possible solution meets your design requirements. Some solutions probably meet more requirements than others. Reject solutions that do not meet the requirements.

• Choose the Best Solution

6. Develop the Solution

Development involves the refinement and improvement of a solution, and it continues throughout the design process, often even after a product ships to customers.

• Development Work
• Storyboards

7. Build a Prototype

A prototype is an operating version of a solution. Often it is made with different materials than the final version, and generally it is not as polished. Prototypes are a key step in the development of a final solution, allowing the designer to test how the solution will work.

• Prototyping

8. Test and Redesign

The design process involves multiple iterations and redesigns of your final solution. You will likely test your solution, find new problems, make changes, and test new solutions before settling on a final design.

• Test and Redesign

9. Communicate Results

To complete your project, communicate your results to others in a final report and/or a display board. Professional engineers always do the same, thoroughly documenting their solutions so that they can be manufactured and supported.

• Final Report
• Display Board
• Science Fair Judging

Try our lesson plans:

• Defining an Engineering Design Problem with Paper Airplanes (Elementary School)
• Teaching Engineering Design with an Egg Drop (Middle School)

Assign a student quiz with Google Classroom:

• Engineering Design Process Quiz (Beginner)
• Engineering Design Process Quiz (Intermediate)

Explore Our Science Videos

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

Engineering Design

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Save to Library
• Last »
• Machine Design Follow Following
• Wind Energy Follow Following
• Engineering Follow Following
• Wind Energy (Engineering) Follow Following
• Engineering Product Design Follow Following
• Mechanical Engineering Follow Following
• Design Engineering Follow Following
• FEM Follow Following
• Product Design Follow Following
• Engineering Mechanics Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Publishing
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Exploring Cutting-Edge Trends: Engineering Research Paper Topics

Table of contents

• 1 How to Choose the Best Engineering Topic for Your Research
• 2.1 Genetic Engineering Research Paper Topics
• 2.2 Nuclear Engineering Research Paper Topics
• 2.3 Research Topics on Security Engineering
• 2.4 Mining and Geological Engineering Research Paper Topics
• 2.5 Mechanical Engineering Research Topics
• 2.6 Materials Engineering Essay Topics
• 2.7 Marine Engineering Research Paper Topics
• 2.8 Industrial Engineering Research Paper Topics
• 2.9 Environmental Engineering Research Paper Topics
• 2.10 Electrical Engineering Research Topics
• 2.11 Computer and Software Engineering Research Topic
• 2.12 Civil Engineering Research Topics
• 2.13 Biomedical Engineering Research Ideas
• 2.14 Automobile Engineering Research Paper Topic
• 2.15 Agricultural Engineering Research Topics
• 2.16 Aerospace Engineering Research Paper Topics
• 2.17 Electrical and Nanoengineering Research Topic
• 2.18 Engineering STEM Research Topics
• 2.19 Engineering Research Topics in Robotics and Automation
• 2.20 Transportation Engineering Research Topics

Embarking on an engineering research paper marks the beginning of a quest for knowledge that could redefine established norms and innovate practices. It’s a thrilling dive into the depths of technical ingenuity and problem-solving. To commence, one must select a beacon—a topic that not only ignites curiosity but also holds the potential to contribute meaningfully to the field. Whether it’s unraveling the complexities of renewable energy systems or exploring the frontiers of nanotechnology, the chosen subject should challenge and inspire. In this realm, precision, relevance, and forward-thinking drive the spirit of inquiry as researchers forge paths that could shape the future of technology.

Selecting the perfect engineering research topics is fundamental in charting a course for breakthroughs and engaging conversations. The purpose of this guide is to help curious individuals find an engineering research topic that fits their interests and the field’s pulse, guaranteeing a journey full of deep learning and significant results.

How to Choose the Best Engineering Topic for Your Research

Choosing the best engineering research topic begins with identifying your areas of interest. Reflect on the subjects that excite you the most and the current issues facing the engineering world. Once you’ve pinpointed your interests, delve into the latest industry trends, advancements, and scholarly discussions. Conferences, journals, and industry publications are gold mines for the newest challenges and innovations that crave exploration.

Next, evaluate the feasibility of the topics on your list. Consider factors such as resource availability, time constraints, and the scope of potential research. Consult with peers and mentors to gauge the relevance and depth of your chosen topic. It’s also wise to factor in the potential for practical application and the contribution your research could make to the field.

Finally, aim for originality. A unique research topic not only stands out but also adds value to the engineering community. By merging your passion with a gap in existing research, you can craft a topic that is both personally rewarding and professionally commendable.

Best Current Research Topics for Engineering

Explore the forefront of innovation with the best current research topics for engineering, a thrilling showcase of groundbreaking ideas poised to redefine technological frontiers and spark transformative advancements in the field.

Genetic Engineering Research Paper Topics

Venture into the realm of genetic engineering, where the potential for innovation intersects with ethical considerations. These engineering research paper topics offer a unique lens into the intricate dance of DNA manipulation and its far-reaching implications.

• CRISPR Cas-9 Precision and its Impact on Genome Editing Techniques
• Gene Therapy Advances for Inherited Disorders
• Synthetic Biology and the Construction of Artificial Life Forms
• Ethical Boundaries in Human Genetic Enhancement
• Genetic Engineering in Agriculture and Crop Resilience
• The Role of Genetic Engineering in Combating Rare Diseases
• Bioprinting Human Tissues for Transplantation and Testing
• Gene Editing’s Potential in Extending Human Lifespan
• Implications of Genetic Privacy in an Era of Genome Editing
• Bioinformatics and the Future of Personalized Medicine in Genetic Engineering

Nuclear Engineering Research Paper Topics

Delving into nuclear engineering offers a glimpse into the powerhouse of energy generation and its safety challenges. The following engineering research topics unpack the complexities of nuclear energy and its role in a sustainable future.

• Advancements in Nuclear Fusion Reactor Design
• Mitigation Strategies for Nuclear Reactor Disasters
• Radioactive Waste Management and Long-Term Containment Solutions
• The Development of Thorium as an Alternative Nuclear Fuel
• Innovations in Nuclear Reactor Safety and Accident Tolerance
• Nuclear Energy’s Role in the Global Transition to Clean Power
• Enhancing Radiation Shielding Techniques for Space Exploration
• Proliferation Risks of Nuclear Materials and Technologies
• Economic Analysis of Lifecycle Costs for Nuclear Power Plants
• Public Perception and Acceptance of Nuclear Energy in the 21st Century

Research Topics on Security Engineering

Security engineering stands at the vanguard of protecting information and infrastructure in our increasingly digital world. These engineering topics to research delve into state-of-the-art defenses and the evolving landscape of threats.

• Quantum Cryptography and the Future of Secure Communication
• Biometric Security Systems and Privacy Implications
• Artificial Intelligence in Cyber Threat Detection and Response
• Blockchain Applications for Decentralized Security Architectures
• Secure Software Development Life Cycle for Emerging Technologies
• Internet of Things (IoT) Security in Smart City Implementations
• Advanced Persistent Threats and Counteracting Network Security Measures
• Social Engineering Attacks and Human-Centric Security Strategies
• Forensic Methods for Detecting Insider Threats
• Risk Management Frameworks for Cloud Computing Security

Mining and Geological Engineering Research Paper Topics

Mining and geological engineering form the bedrock of our quest for natural resources, balancing extraction techniques with environmental stewardship. Here are vital engineering topics to write about that address today’s challenges and future solutions.

• Autonomous and Remote-Controlled Mining Machinery Innovations
• Environmental Impact Assessments of Hydraulic Fracturing Practices
• Geostatistical Analysis of Mineral Resource Estimation
• Slope Stability and Landslide Prevention in Open-Pit Mines
• The Application of Geospatial Technologies in Mineral Exploration
• Mine Rehabilitation and Post-Mining Ecosystem Restoration
• Advancements in Offshore Drilling Technology and Impact Mitigation
• Earthquake Prediction Models and Mining Induced Seismicity
• Rare Earth Element Extraction Techniques and Economic Viability
• Subsidence Engineering and Mitigation in Underground Mining Operations

Mechanical Engineering Research Topics

Mechanical engineering is the cornerstone of innovation, driving forward advancements in technology and industry. These engineering topics for research paper explore the cutting-edge developments and challenges within the mechanical realm.

• 3D Printing of Biodegradable Materials for Sustainable Manufacturing
• Nanotechnology in Mechanical Engineering: Enhancing Material Properties
• Robotics and Automation in Precision Assembly Lines
• Energy Harvesting Techniques for Self-Powered Electronic Devices
• Fluid Dynamics Analysis in Reducing Aerodynamic Drag for Vehicles
• Wearable Technology Innovations for Human Performance Monitoring
• Advanced Composite Materials for Aerospace Application Efficiency
• Thermal Management Systems in Electric Vehicle Battery Packs
• Vibration Analysis for Predictive Maintenance in Heavy Machinery
• Bioinspired Design: Mimicking Nature for Mechanical Solutions

Materials Engineering Essay Topics

Materials engineering is at the forefront of technological progress, shaping the way we build the future with innovative substances and composites. These engineering essay topics delve into the synthesis, analysis, and application of materials that could revolutionize industries.

• Graphene Integration in Electronics and Energy Storage Devices
• Biodegradable Polymers in Sustainable Packaging Solutions
• Self-healing Material Technologies and Their Long-term Durability
• Advanced Ceramics for High-Temperature Structural Applications
• Nanomaterials for Targeted Drug Delivery Systems
• Smart Textiles in Wearable Technology and Their Functionalities
• Metallic Glass Synthesis for Industrial Application
• Corrosion Resistance Strategies in Marine Engineering Materials
• High Entropy Alloys and Their Mechanical Properties
• Photovoltaic Materials for Enhanced Solar Cell Efficiency

Marine Engineering Research Paper Topics

Marine engineering embodies the spirit of exploration and innovation, navigating the challenges of the sea with advanced technology and design. The following topics in engineering dive deep into the ocean’s mysteries and the engineering solutions that sustain life and commerce on the waves.

• Wave Energy Conversion Systems and Coastal Power Generation
• Hull Design Optimization for Fuel Efficiency in Cargo Ships
• Ballast Water Treatment Technologies to Combat Marine Invasions
• Underwater Acoustic Communication Systems for Submersible Vehicles
• Corrosion Resistant Materials for Prolonged Marine Infrastructure Lifespan
• Autonomous Marine Vehicles and Their Navigational Algorithms
• Impact of Climate Change on Ship-Borne Disease Spread
• Sustainable Fishing Techniques and Equipment Design
• Arctic Drilling Equipment and Ice Management Strategies
• Marine Robotics for Deep-Sea Exploration and Resource Extraction

Industrial Engineering Research Paper Topics

Industrial engineering is a nexus of productivity, efficiency, and innovation, integrating complex systems and processes. These interesting engineering topics dissect the intricacies of industry operations and the pursuit of technological advancements for systemic improvements.

• Ergonomic Design in Manufacturing Workstations to Boost Efficiency
• Machine Learning Applications for Supply Chain Optimization
• System Dynamics Modeling for Predictive Production Planning
• Green Manufacturing Practices and Circular Economy Integration
• Human-robot Collaboration and Safety in the Workplace
• Quality Control Enhancements through Statistical Process Control
• Lean Manufacturing Techniques and Waste Reduction Strategies
• Smart Factory Implementations in Industry 4.0
• Simulation of Logistics Networks for Urban Congestion Alleviation
• Cognitive Ergonomics in Industrial Systems Design

Environmental Engineering Research Paper Topics

Environmental engineering is a vital subset of civil engineering, dedicated to creating harmony between construction and the natural world. These topics focus on sustainable development and ecological preservation within the built environment.

• Phytoremediation Techniques in Soil and Water Decontamination
• Carbon Capture and Storage Solutions in Urban Planning
• Impact of Green Roofs on Urban Microclimates
• Sustainable Wastewater Treatment and Reuse Strategies
• Air Quality Management and Pollution Control in Metropolises
• Eco-friendly Concrete Alternatives in Civil Construction
• Bioreactor Landfills and Methane Harvesting Technologies
• Riverbank Filtration Systems for Potable Water Supplies
• Noise Pollution Reduction in Highway Engineering
• GIS Applications in Hazardous Waste Site Remediation

Electrical Engineering Research Topics

Electrical engineering propels countless innovations, from microelectronics to massive power grids. The following topics highlight the dynamic and essential developments reshaping the electrical landscape.

• Wireless Power Transfer Systems for Electric Vehicle Charging
• Organic Photovoltaic Cells for Improved Solar Energy Harvesting
• Nano-electromechanical Systems in Medical Device Engineering
• Energy Storage Solutions in High-Density Lithium-Ion Batteries
• Smart Grid Technologies for Distributed Energy Resources Management
• Electromagnetic Field Effects on Human Health
• Machine Vision Algorithms for Automated Quality Inspection
• Flexible Electronics for Wearable Technology Applications
• High-frequency Trading Algorithms and Market Impact Analysis
• Quantum Computing and Its Role in Cryptography

Computer and Software Engineering Research Topic

Computer and software engineering stands at the cutting edge of innovation, constantly evolving to meet the demands of a digital future. These software engineering research topics delve into the algorithms, systems, and applications driving progress in this ever-expanding field.

• Agile Methodologies Impact on Software Development Lifecycle
• Cybersecurity in Cloud Computing Environments
• Application of Artificial Intelligence in Automated Code Generation
• Blockchain Technology Beyond Cryptocurrency
• Human-Computer Interaction and User Experience Optimization
• Internet of Things Security Protocols for Smart Home Systems
• Machine Learning Techniques in Predictive Software Analytics
• Virtual Reality Integration in Software Testing Environments
• Software Solutions for Big Data Management and Analysis
• Ethical Implications of Autonomous Decision-making Systems

Civil Engineering Research Topics

Civil engineering is a pillar of societal development, encompassing the design and construction of infrastructure that underpins our daily lives. The research topics in civil engineering listed below address the contemporary challenges and technological strides shaping the field’s future.

• Seismic Retrofitting Techniques for Aging Infrastructure
• Sustainable Urban Drainage Systems and Flood Risk Mitigation
• Smart Materials for Self-repairing Concrete Structures
• Advanced Geotechnical Methods for Landslide Prevention
• The Role of Civil Engineering in Urban Heat Island Reduction
• Lifecycle Assessment of Green Building Materials
• Integration of Autonomous Vehicles into Urban Traffic Management
• 3D Printing in Rapid Construction and Design Prototyping
• Water Reclamation and Reuse in Megacities
• Innovations in Bridge Engineering for Enhanced Longevity and Durability

Biomedical Engineering Research Ideas

Biomedical engineering merges the intricate world of medicine with the precision of engineering, opening new frontiers in healthcare. While these are not topics, they embody a similar spirit of technical innovation applied to biological systems.

• Tissue Engineering Strategies for 3D-Printed Organs
• Wearable Biosensors for Real-Time Health Monitoring
• Nanorobots in Targeted Drug Delivery Systems
• Neural Engineering for Brain-Machine Interface Development
• Biocompatible Materials for Implantable Medical Devices
• Advanced Prosthetics Controlled by Electromyographic Signals
• Artificial Intelligence in Diagnostic Imaging Techniques
• Biomimicry in Medical Device Design
• Regenerative Medicine and Stem Cell Therapy Applications
• Computational Modeling for Personalized Medicine Treatment Plans

Automobile Engineering Research Paper Topic

Automobile engineering is continuously evolving, driven by the quest for sustainability, efficiency, and cutting-edge technology. Although distinct from software engineering topics for research, these themes share a focus on innovation and design in the quest for advancement.

• Electric Vehicle Battery Management Systems for Optimal Performance
• Autonomous Vehicle Sensor Integration and Data Fusion
• Hydrogen Fuel Cell Advancements for Zero-Emission Cars
• Aerodynamic Design for Enhanced Fuel Efficiency in Commercial Vehicles
• Advanced Driver-Assistance Systems and their Impact on Traffic Safety
• Smart Materials for Lightweight and Durable Automotive Components
• Integration of IoT in Vehicle-to-Vehicle Communication Systems
• Predictive Maintenance in Automotive Engineering using Machine Learning
• Noise, Vibration, and Harshness Reduction Techniques in Car Design
• The Impact of Vehicle Electrification on Urban Planning and Infrastructure

Agricultural Engineering Research Topics

Agricultural engineering fuses the knowledge of engineering with agricultural practice to solve crucial challenges in food production and farming sustainability. While not inherently controversial engineering topics, these subjects often stir debate due to their significance in global food security and environmental impact.

• Precision Farming Technologies to Maximize Crop Yield
• Water Resource Management for Sustainable Irrigation Practices
• Genetic Engineering of Crops for Climate Resilience
• Renewable Energy Systems in Agriculture
• Robotics and Automation in Precision Livestock Farming
• Post-Harvest Technology for Reducing Food Loss
• Soil Health Monitoring Techniques for Enhanced Nutrient Management
• Agrochemicals Delivery Systems and Their Environmental Footprint
• Controlled Environment Agriculture for Urban Farming Efficiency
• Bioenergy Production from Agricultural Waste Management Systems

Aerospace Engineering Research Paper Topics

Aerospace engineering takes us beyond the confines of Earth, embracing the vastness of space with technologies that defy gravity. These topics, while distinct from genetic engineering research topics, are similarly ambitious, exploring the limits of human ingenuity and the potential for discovery beyond our atmosphere.

• Materials Engineering for High-Stress Aerospace Applications
• Computational Fluid Dynamics in Hypersonic Vehicle Design
• Satellite Swarm Navigation Techniques for Space Exploration
• Innovative Propulsion Systems for Deep Space Missions
• Bioastronautics: Sustaining Life in Space Environments
• Impact of Microgravity on Mechanical Systems Design
• Unmanned Aerial Vehicle Aerodynamics for Mars Reconnaissance
• Thermal Protection Systems in Re-entry Vehicle Engineering
• Design Optimization of Spacecraft Life Support Systems
• Advanced Rocketry and the Viability of Space Tourism

Electrical and Nanoengineering Research Topic

Electrical engineering is a dynamic field that encompasses the study and application of electricity and electronics, propelling countless modern innovations. These topics extend into the realm of topic, probing into the minutiae of materials and processes that power our electronic devices.

• Nanoscale Semiconductor Devices for Next-Generation Computing
• Organic Light-Emitting Diodes in Flexible Display Technology
• Quantum Dot Solar Cells for Enhanced Photovoltaic Efficiency
• Nanomaterials in High-Density Energy Storage Solutions
• Nano-antennas for Improved Wireless Communication Systems
• Magnetic Nanoparticles in Medical Imaging and Diagnostics
• Nanotechnology in Electromagnetic Interference Shielding
• Nanostructured Materials for Advanced Sensor Technologies
• Nanofabrication Techniques for Superconducting Electronics
• Energy Harvesting at the Nanoscale for Self-Powered Devices

Engineering STEM Research Topics

Engineering STEM is an ever-expanding field, pivotal to the advancements in how we connect and interact with technology. These engineering STEM research topics address the latest innovations and challenges in creating more efficient, robust, and sophisticated communication systems.

• 5G Network Infrastructure and its Socioeconomic Impacts
• Machine Learning Algorithms for Enhanced Signal Processing
• Organic Transistors in Flexible Electronics
• Wearable Communication Devices for Health Monitoring
• Low Earth Orbit Satellite Constellations for Global Internet Coverage
• Signal Encryption Techniques for Secure Communication Channels
• Energy-Efficient Routing Protocols in Mobile Ad-hoc Networks
• Integration of LiFi for Next-Generation Wireless Communication
• Quantum Computing’s Role in Advancing Cryptography
• The Evolution of Underwater Acoustic Sensor Networks

Engineering Research Topics in Robotics and Automation

Robotics and automation stand at the forefront of engineering, blending artificial intelligence with mechanical prowess to innovate how tasks are performed. These research topics delve into the transformative potential of robots and automated systems, from intricate surgeries to industrial assembly lines.

• Swarm Robotics Coordination Algorithms for Disaster Relief Operations
• Collaborative Robots and Human-Robot Interaction Safety Protocols
• Adaptive Control Systems for Precision Agriculture Robotics
• Augmented Reality in Enhancing Robotic Assembly Line Training
• AI-Driven Predictive Maintenance in Industrial Automation
• Soft Robotics Applications in Minimally Invasive Surgery
• Development of Energy-Efficient Actuators for Sustainable Robotics
• Machine Vision Systems for Quality Control in Manufacturing
• Robotic Exoskeletons for Rehabilitation and Enhanced Mobility
• Automation in Smart Grids for Optimized Energy Distribution

Transportation Engineering Research Topics

Transportation engineering is a key driver in the advancement of mobility solutions, focusing on the design, construction, and maintenance of efficient transport systems. These research topics investigate the development of safer, more sustainable, and technologically advanced transportation networks.

• Impact of Autonomous Vehicles on Urban Traffic Flow
• Eco-Friendly Pavement Materials and Their Lifecycle Assessment
• Smart Traffic Signal Systems for Reduced Congestion
• Electrification of Public Transit and Infrastructure Challenges
• Pedestrian Flow Dynamics in Urban Planning
• Bridge Health Monitoring Using IoT Sensors
• High-Speed Rail Systems and Cross-Border Integration Challenges
• Adaptive Cruise Control Systems in Vehicle Safety Enhancement
• Drone Technology in Expedited Cargo Delivery
• Multi-Modal Transportation Planning for Improved Accessibility

Readers also enjoyed

WHY WAIT? PLACE AN ORDER RIGHT NOW!

Just fill out the form, press the button, and have no worries!

We use cookies to give you the best experience possible. By continuing we’ll assume you board with our cookie policy.

UCL Institute for Environmental Design and Engineering

• Undergraduate
• Master's
• Scholarships
• What our students say
• Short courses
• Partnerships

Virtual Decision Rooms for Water Neutral Urban Planning (VENTURA)

Ventura is a collaborative research project between Imperial College (ICL), University College London (UCL) and the British Geological Survey (BGS).

15 April 2024

The VENTURA project  is schedule to run from October 2021 to April 2024) and was funded by the UK Research and Innovation (UKRI) under the Engineering and Physical Sciences Research Council (EPSRC) as part of a programme entitled, 'Digital Economy: Sustainable Digital Society'.

The project team for this collaborative project include academics from ICL, UCL and the BGS and the UCL Ventura team is led by Nici Zimmermann , Ke (Koko) Zhou ,  Irene Pluchinotta  and Pepe Puchol-Salort . The broader aim of Ventura is to support water neutrality decisions through digital tools.

The UCL team led the participatory systems thinking activities in case studies in Greater Manchester and Enfield, London. They engaged with a range of stakeholders including the trilateral group, namely Greater Manchester Combined Authority, United Utilities, and the Environment Agency. They also engaged with local boroughs, the Greater London Authority, and broader environmental stakeholders. The aim of the project's stakeholder engagement activities is to understand stakeholders’ experience and perspectives of the water neutrality governance challenges and complexities, supporting the exploration of the interconnections between hydrological and institutional dynamics towards sustainable growth.

The team employed comprehensive participatory approaches: identifying challenges with stakeholders and mapping the complex core problem. They developed connection circles and causal loop diagrams (CLDs) representing the system's complexity. The team drew from systems thinking, behavioural operational research, and organisational management studies (institutions, attention and emotions) to inform their reflections on the CLDs. They also analysed the decision-making process and compared stakeholders’ perceived boundaries of the system, specifically on how different groups of stakeholders perceive the system differently, and their reflections to achieve integrated decision-making.

Figure: Manchester workshop, causal loop diagrams developed by stakeholder groups

In their collaborated with Imperial College London and the British Geological Survey integrating systems modelling and CLDs in a digital tool, the UCL team called a virtual decision room (VDR) for the Manchester case study. They used this combination of systems thinking with GIS-based modelling in a virtual decision room to enhance practical decision-making by offering an easily accessible interface. This integration facilitates a holistic understanding of specific policy issues and broader governance challenges.

The VENTURA project operated in an highly interdisciplinary setting, and it followed principles of transdisciplinary, and they also used it as a basis to reflect on the process of collaborating across disciplines and working with stakeholders—with the aim to better understand the development of digital tools in participatory, inter- and transdisciplinary contexts.

For more information about the project, please visit following resources. If there are specific questions about the project, you are welcome to contact the team directly.

Figure: the VENTURA virtual decision room

Publications and conference papers:.

VENTURA - virtual decision rooms for water neutral planning  https://nora.nerc.ac.uk/id/eprint/535476/

Comparisons of Emotional Boundaries: A case study of water neutrality and sustainable urban development in London: https://discovery.ucl.ac.uk/id/eprint/10180093/

Other documents:

Ventura VDR and the CLD:  https://ventura.bgs.ac.uk

Ventura project summary and more details about the development of CLD:  https://www.imperial.ac.uk/systems-engineering-innovation/research/ventura/

Ventura Github:  https://github.com/ventura-water

Researchers (with profile links):

Ke (Koko) Zhou

Irene Pluchinotta  

Nici Zimmermann

Pepe Puchol-Salort

Image credit: pexels.com

Te Kura Mata-Ao | School of Engineering

Innovation that guides our future.

Te Kura Mata-Ao School of Engineering

Eight engineering programmes.

At Waikato, we offer a full-range engineering programmes including civil, mechanical, chemical, and electrical engineering. These programmes are internationally accredited through the Washington Accord and recognised around the world.

Why study engineering at Waikato

Study with us, bachelor of engineering (hons), master of engineering, master of engineering practice, engineering research groups, engineering design show.

The annual Waikato Engineering Design Show celebrates the fine achievements of our undergraduate professional engineering students. The event is open to the public and a great opportunity to see some cutting-edge tech in action.

Engineering jump start programme

If you're passionate about engineering but do not quite meet the BE(Hons) entry requirements or want a refresh of NCEA Level 3 maths and physics, join us for the Engineering Jump Start programme from late January on the Hamilton campus.

Add a business edge

Waikato offers a unique opportunity for students to gain engineering expertise and business acumen through the Diploma in Engineering Management alongside their BE(Hons), preparing them for top management roles in industry.

Engineering Scholarships and Awards

Find out more about the scholarships we have available.

What our students are saying

Meet the staff of the school of engineering.

Family footsteps to engineering excellence: Shermi Perera's graduates with honours

Bachelor of Engineering graduate, Shermi Perera, is working at Beca in Dunedin, engaging in diverse engineering projects aimed at enhancing water resource management.

Waikato's engineering degrees receive international accreditation

Read about the international accreditation the University of Waikato has received for all eight of its Bachelor of Engineering (Honours).

Waikato mechatronics connecting with the world

Dr. Shen Hin Lim, Senior Lecturer of Mechatronics and Programme Leader at the University's School of Engineering, eagerly anticipates a peaceful summer after frequent trips to Korea.

Significant funding boost for University of Waikato research

The University of Waikato has achieved significant results in the latest funding round from the Ministry of Business, Innovation and Employment's Endeavour Fund.

Contact the School of Engineering

You’re viewing this website as a domestic student.

You’re currently viewing the website as a domestic student, you might want to change to international.

You're a domestic student if you are:

• A citizen of New Zealand or Australia
• A New Zealand permanent resident

You're an International student if you are:

• Intending to study on a student visa
• Not a citizen of New Zealand or Australia