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Experimental rigs for testing components of advanced industrial applications

This paper presents experimental rigs of the Research Centre for the Mechanics of Turbomachinery of the Department of Civil and Industrial Engineering of the University of Pisa. Most of them were designed and constructed to allow investigations of real machine components and to furnish more realistic results than basic tribological test rigs.

Tilting pad journal bearings, as well as gears and complete gearboxes for advanced industrial applications, can be tested using the rigs described in the paper. A novel test rig with a power rating of approximately 1 MW allows investigations of the static and dynamic characteristics of high-performance tilting pad journal bearings for turbomachinery. A twin disc machine and closed loop gear test rig are used to investigate the different kinds of wear mechanisms occurring in gears. Functional and durability tests on planetary gearboxes for new turbo-fan engines could be performed using another novel large test rig. A circulating power configuration was adopted for most of the rigs so that only the power needed to cover the friction losses has to be supplied, while the circulating power can be more than 20 times higher. All the test rigs include very complex load applications and lubrication plants, as well as dedicated control and data acquisition systems.

The rigs and related plants were designed and constructed through strong and fruitful collaborations between the university and some large and small–medium companies. Despite some limitations in the publication of the results as a result of the industrial sensitivity of the data, the synergy among these different actors was stimulating and fundamental for the realization of new advanced industrial applications.

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Open Access: The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Quality assurance through a robust and flexible system developed in close cooperation with you.

What is a Test Rig?

A test rig is a complete system that controls, measures, and evaluates a device according to defined testparameters. It includes both hardware and software. Sensors, instruments, and actuators are integrated using data acquisition hardware or protocols. The software handles all interfaces to hardware and other systems, as well as the user interface for the operator.

A test rig can be customized to tackle a multitude of difficult measurement tasks necessary to fulfill your specific requirements and needs. It should be built in a reliable and flexible manner with components verified in previous systems. It should also be user-friendly, with a simple user interface and an ergonomic design.

EXAMPLES OF TEST RIG APPLICATIONS

• Mechanical and electrical
• Long duration and stress
• Hardware in the Loop (HIL)
• Optical inspection using camera or other sensors
• Flow, temperature, pressure, and leakage
• Conductivity and humidity

TEST RIG EXAMPLES:

• Inspection using camera or other optical sensors
• Flow, temperature, pressure and leakage
• Conductivity, humidity and many more…

Benefits with a Test Rig

Quality Assurance

To assure product quality, testing is needed in both R&D and production. Automated testing results in a larger test coverage and greater reliability than manual testing, which in turn leads to better and more robust products for your end customer.

Cost Efficiency

Finding design issues early in the development process saves time and money. The same goes for production where it is important to find errors early in the process, particularly before the customer discovers it.

Time to Market

Developing test rigs in parallel with product development enables early testing, reducing the risk of finding critical issues late. The final product testing phase at the end of the project can also be reduced significantly. 

What we at DVel do

DVel takes the overall responsibility and delivers complete turn-key systems for your testing needs. You can rely on the systems from DVel for many years, due to their robustness, their stable hardware platform, and well tested software development modules. By using a modular approach for both hardware and software, we build robust and flexible systems, enabling adaptations to future products and needs.

Our customers always come first, and we want you to be involved through the whole process of developing a test rig. In order to do that, we schedule several meetings to discuss and follow-up on your project. By doing this, we are making sure that you will get the system that you have envisioned.

Test Rigs we have built!

CASE STUDIES

Examples of test rigs.

Automating Train Brake Testing

Together with Faiveley Transport Nordic (FTN) DVel developed automated test rigs for brakes within the rail industry. Since FTN wanted to design the hardware setup and build the rig, DVel delivered the instrumentation and the software. The outcome was so successful that FTN are now producing four more copies of the same test rig and are also considering adopting the DVel software as a platform also for several other test systems.

”This project has been a truly joint venture between Faiveley and DVel. The close collaboration and spirit of team work has meant that we were able to tackle all obstacles swiftly, avoid delays and improve the end result.”

Per Renmark, Operations Director, Faiveley Transport Nordic

Test Rigs for Evaluating Components for Medical Equipment

In the medical technology industry, it is vital that equipment does not fail when used. A failure of equipment may have a serious or even fatal outcome. There is thus a need for extensive test equipment during the R&D phase of each new project that can test the components, the system, and associated consumables. DVel has delivered several test rigs for component evaluation and performance testing. Together we have developed a close partnership, built on trust, open communication, and close cooperation.

A Test Rig for Hydronic Actuators

For ESBE, DVel developed a Test Rig for Hydronic Actuators. ESBE is a leading manufacturer of hydronic control equipment with a wide range of systems, products, and system solutions. They needed a simple, efficient, and effective test system for test and evaluation of warranty claims on their rotary actuators. DVel develped a system according to ESBE’s needs.  Configuration of the unit being tested can be fast and the system can easily be maintained. The system quickly became an important asset in the quality assurance strategy of ESBE.

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Methodology

• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Effects of varied tillage practices on soil quality in the experimental field of red-soil sloping farmland in southern china.

1. Introduction

2. materials and methods, 2.1. study site description, 2.2. experimental design and analyses of soil-sampling indicators, 2.3. soil quality evaluation methods, 2.3.1. establishment of the mds, 2.3.2. soil quality index calculation (sqi), 2.3.3. accuracy verification of sqi evaluation, 2.4. data analyses, 3.1. changes in soil physical properties under different tillage practices, 3.1.1. soil water-retention characteristics, 3.1.2. soil aggregate characteristics, 3.2. changes in soil chemical properties under different tillage practices, 3.3. changes in soil quality under different tillage practices, 3.3.1. establishing a mds for soil quality assessment, 3.3.2. rational validation of mds and soil quality evaluation, 4. discussion, 4.1. effect of tillage practices on soil quality in red-soil slopes, 4.2. effect of crop growth stages on soil quality in red-soil sloping farmland, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Yan, K.; Li, J.; Li, J.; Chen, Z.; Zhang, C.; Wang, D.; Hu, Y.; Wang, Z. Effects of Varied Tillage Practices on Soil Quality in the Experimental Field of Red-Soil Sloping Farmland in Southern China. Sustainability 2024 , 16 , 7843. https://doi.org/10.3390/su16177843

Yan K, Li J, Li J, Chen Z, Zhang C, Wang D, Hu Y, Wang Z. Effects of Varied Tillage Practices on Soil Quality in the Experimental Field of Red-Soil Sloping Farmland in Southern China. Sustainability . 2024; 16(17):7843. https://doi.org/10.3390/su16177843

Yan, Keyu, Jing Li, Jianxing Li, Zhengfa Chen, Chuan Zhang, Daoxiang Wang, Yanmei Hu, and Zhongliang Wang. 2024. "Effects of Varied Tillage Practices on Soil Quality in the Experimental Field of Red-Soil Sloping Farmland in Southern China" Sustainability 16, no. 17: 7843. https://doi.org/10.3390/su16177843

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Development of a Refrigeration System Test Rig

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• Ester Angula 11 ,
• Fillemon Nangolo 11 ,
• Mutiu Erinosho 11 &
• Sam Shaanika 11  

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Thermodynamics is an important aspect of mechanical engineering, and the fundamental part of it is refrigeration. It is often beneficial for students of mechanical engineering to observe practically at the working principle of a refrigeration system. Thus, this paper presents the development of a vapour compression refrigeration system test rig to be used for student’s practical work in the mechanical engineering laboratory at engineering campus, University of Namibia. The refrigeration system was designed to have a refrigeration capacity of 25 kW and to provide cooling at a desired temperature of 16 °C. The calculations for the refrigeration system were based on a thermodynamic analysis of the components, which makes use of the principles of conservation of energy. The system’s theoretical Coefficient of Performance (COP) was found to be 9.19. A 3D CAD model of the test rig was developed and the layout of the test rig was kept simple with all components clearly identified.

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Experimental and Theoretical Investigations of Single- and Two-Phase Refrigeration Systems

Investigation on the Performance of the Pump-Free Double Heat Source Ejector Refrigeration System with R1234yf

Thermodynamic Analysis of a Refrigeration System Operating with R1234yf Refrigerant

Abbreviations.

Refrigeration or condenser capacity [W]

Mass flow rate [kg/s]

Enthalpy [kJ/kg]

Compressor power [kW]

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Department of Mechanical and Industrial Engineering, University of Namibia, Ongwediva, Namibia

Ester Angula, Fillemon Nangolo, Mutiu Erinosho & Sam Shaanika

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Correspondence to Ester Angula .

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Department of Mechanical Engineering, Universiti Teknologi Petronas, Seri Iskander, Perak, Malaysia

Mokhtar Awang

Department of Mechanical Engineering, Universiti Malaya, Kuala Lumpur, Malaysia

Seyed Sattar Emamian

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Angula, E., Nangolo, F., Erinosho, M., Shaanika, S. (2021). Development of a Refrigeration System Test Rig. In: Awang, M., Emamian, S.S. (eds) Advances in Material Science and Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-3641-7_6

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DOI : https://doi.org/10.1007/978-981-16-3641-7_6

Published : 06 July 2021

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