What Are the Effects of the Different Design Parameters When Building a Linear Motor?

Good question.

You earn a PhD by the time you get a half good answer :-).More detail about what you are wanting to achieve wouldhelp muchly. Stroke, force, speed, ... . Appliaction?Too much to cover at present. This is just a quick canter and their should be others more expert here. As well as "learned papers" you can get a good idea by using eg Google image search using eg linear motor as search term, looking for manufacturers "strutting their stuff" re new models, advantages, capabilities etc and seeing what they say. But:Air gap is crucial.

Magnetic field varies with inverse cube of distance at a distance (really) due to interaction of two poles working in inverse square. (FWIW few people realise that this is why what happens happens). Magnet shape and thickness and proximity to other magnets affect the field. At distances up to 5 or 10 magnet thicknesses if you want to see what the field is at a point you are best to use one of the many free and far from free simulation programs. For practical purposes for DIY linear motors alternators etc you can use the rule of thumb that the magnet is about as effective as it's going to be out to about half its thickness. eg if you use a really top grade "rare earth" magnet (details later maybe) you can get around 1T at half it's thickness. So for "ironless" coils if you are using 10mm thick magnets then the majority of your coil wants top be no more than 5mm away from the pole faces so if you have a 1mm airgap then you can have 4mm thick coils.

Which is why people use laminations and why ironless construction has very flat coils. Once you add laminations in the windings you "extend the field" at the cost of more mass, eddy current losses, magnetic saturation, saliency (cogging) unless you are very good at mechanical design, and other secondary effects. About all the rest is standard theory at a first approximation. You can find papers on motor design, ampturns, conductor resistance (which affects heating and current you can provide to get the amp-turns that do the work. If you use laminations you are into the wonderful world of saturation, BH curves, losses, fringing effects, ... . But it's all available in the standard motor texts. As a simple simplistic starting position, to get maximum motor force you are trying to use the strongest magnets you cn get, smallest airgaps, flattest coils, and maximise amp turns. Amp turns are affected by the copper (usually) you can fit in the available space and the current you can apply and the heating that results.

• Related Questions

Fat Tailed risks: do they get fatter when we linearize non-linear systems?

Absolutely! There are many, such as for example:Ahlfeld R, Belkouchi B, Montomoli F, 2016, SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos, Journal of Computational Physics, Vol:320, ISSN:0021-9991, Pages:1-16Montomoli FF, Amirante DD, Hills NN, Shahpar SS, Massini MM. Uncertainty Quantification, Rare Events, and Mission Optimization: Stochastic Variations of Metal Temperature During a Transient. ASME. J. Eng. Gas Turbines Power. 2014;137(4):042101-042101-9. doi:10.1115/1.4028546Ahlfeld R, Montomoli F, Scalas E, Shahpar S, 2016, Uncertainty Quantification for Fat-Tailed Probability Distributions in Aircraft Engine Simulations, Journal of Propulsion and Power Montomoli, F and Massini, M, Gas turbines and uncertainty quantification: Impact of PDF tails on UQ predictions, the Black Swan, GT2013-94306, ASME Turbo Expo 2013Note that there's nothing inherently wrong in using polynomials to model Fat-Tailed risk - the problem is in the pdf of the predictors. Consider a lognormal distribution: strictly speaking this is not fat-tailed, since the tail goes to zero faster than a power, but just heavy-tailed (the tail goes to zero slower than an exponential). The log-normal distribution has an associated family of orthogonal polynomials, but as described in this answer, they are not dense in the space of mean-square integrable functions. This means that expanding the response of a complex system, whose input variables are log-normally distributed, in a series of polynomials which are orthogonal w.

r.

t. the log-normal measure, is not a good idea: the expansion may not converge or converge to a limit which is not actually the response function of the system. However, even with a fat-tailed distribution of the inputs (for example the Cauchy distribution), you can still define numerically a set of polynomials which are orthogonal to the input distribution, as long as you truncate the distribution to some limit. This is an approximation, of course, but it could make sense. Example: you want to model the possibility of a temperature spike at the inlet of a gas turbine nozzle. It might make sense to assume that rare events (very high temperature spikes) have a frequency which is much higher than expected, if the distribution of temperature spikes were Gaussian. Thus, you may want to use a lognormal, t-Student or Cauchy distribution (t-Student with 1 degree of freedom). On the other hand, you clearly don't need to model the risk posed by spikes of, say, $10^4 K$: even if, from a theoretical point of view, the untruncated Cauchy distribution would give a "high" (w.r.t. a Normal distribution) probability of such spikes, in practice there's no way at all that you would reach a temperature of $10^4 K$ inside a gas turbine, except pheraps if the gas turbine were hit by an atomic bomb :) Thus, you can safely truncate the Cauchy to a limit which is sufficienly higher than any physically plausible temperature, but lower than $infty$

------

What propulsion system would not pollute the surface when landing on a pristine celestial body?

Wikipedia says that Ryugu (for example) has a mass $M_ryu$ of 4.510 kg and a radius $R_ryu$ of about 450 meters. With G 6.674 10 m kg s that makes the surface gravity about 1.

48 10 m s. You could use either big ion thrusters using a noble gases angled at /- 45 degrees to bring you within 200 meters of the surface without much ion sputtering. Unlike cold gas thrusters, the ion beams accelerated to say 100,000 eV would have a very narrow emission angle, roughly the square root of the ratio of the ion plasma thermal energy to the acceleration energy for a design optimized for the task. $$ theta approx sqrtfrack_B T_ionVe approx sqrtfrac1100,000 approx 0.

2 $$1 eV corresponding to about 10,000 K sounds pretty hot for an ion temperature, this is probably a conservative number. See for example JPL/Descanso Fundamentals of Electric Propulsion: Ion and Hall Thrusters Dan M. Goebel and Ira Katz If the ion beam spread out due to self-propulsion, you might start with a wide exit aperture and try to play tricks with attraction to a central electron beam which you need for spacecraft charge neutrality.

For an $m$600 kg satellite like Hyabusa 2 at this range, each thruster would need a thrust of$$T fracsqrt22 Gfracm M_ryu(R_ryu200)^2 approx 45 textmN $$which is only about half the thrust of one of DAWN's three main ion thrusters. Shutting off the thrusters the spacecraft would fall from it's 200 meter hovering altitude towards the surface. We can get the terminal velocity by conserving energy $Delta T Delta U 0$.

The kinetic energy gained at impact would be:$$Delta T_i -Delta U m M_ryu G left( frac1R_ryu - frac1R_ryu200 right) approx 12.3 textJoules,$$and so the velocity at impact is $$v_i sqrtfrac2 Tm approx 20 textcm/sec.$$As suggested by the OP in the question, you could soak that up with the landing gear. You could do that with some combintation of thing like:You could also add one or three thrusters (noble ion or not) on the backside of the spacecraft pushing you into Ryugu, in order to cancel any possible recoil if you latched your springs too soon or your legs didn't all touch at the same time or there was a more complex landing involving shifting rocks or gravel. You'd use three instead of one to cancel angular momentum resulting from the legs hitting at different times.This is why my favorite option is the linear motor/generator with dynamic braking in each leg. You can wait until you have something like three point contact before starting active deceleration. Servo control can be really smart and fast with modern electronics and inertial sensors

------

Mobile robot speed synchronization for straight line moving

There are really two problems here:Suppose you get the "perfect" control scheme that sends exactly the correct signals to the motors. The problem then becomes: What if one wheel runs over a piece of paper and just spins for a little bit instead of generating traction? What if one wheel runs over some dust that sticks to the tire and now it's a little bigger than the other wheel?I'm assuming here that your wheel odometry is coming off of encoders on the motor, but the same arguments are true if you've got an idle wheel to do odometry measurements. Assuming some rotation of the motor $theta$, each wheel should traverse a distance of $rtheta$, where $r$ is the wheel radius. However, if one wheel is slightly larger than the other $(repsilon)$, then one wheel traverses $rtheta$ while the other traverses $(repsilon)theta$, or $rtheta epsilon theta$.Your vehicle will then turn (assuming a differentially steered or two-wheel robot) an angle of $psi epsilon theta / d$, where $d$ is the wheel base (distance between the two wheels. You can see now that the angle your vehicle turns, $psi$, is a linear function of how far your wheels turn, $theta$. I have posted an answer like this before - The only way you can be sure to drive in a straight line is to measure where the robot is relative to the straight line. This could be LIDAR, a localization routine (like SLAM), overhead webcam watching the robot, compass/magnetometer, etc. There will always be variations that prevent your vehicle from going exactly straight, so you need to be able measure how you're travelling and be able to adjust accordingly. With regards to your original question though, first I'll comment that you're looking to provide only a wheel speed, so if anything it's multi-input single-output. If you're looking for someone to comment specifically on the block diagrams you've provided, then you need to explain what they mean. Typically the symbols $x$, $v$, and $a$ are used for linear position, speed, acceleration, respectively, and $theta$, $omega$, $alpha$ are used for angular position, speed, and acceleration. You use $v$* and $omega$*, which looks like linear speed and angular speed, so I don't know what your inputs are, or what the star means, or why there aren't any integrator or derivative blocks (how are you getting between position and speed?), or why Structure 1 has G1 and G2 where Structure 2 has G1 and G1, etc.If you want to drive in a straight line without measuring the orientation of the vehicle, send the same speed signal to both motors. That's probably the best you can do

Articles recommandés
Yes, That Elephant Can Dance: General Motors Chief Talent Officer on Innovation at Scale
The challenge of injecting innovation into large, staid, and stalled organizations has long vexed leaders, consultants, and academics. The list of failed efforts goes on and on, including Yahoo!, Motorola, Blackberry, Sears, HP, Kodak, RadioShack, and that terrible merger between Chrysler and Mercedes-Benz. Yet there are exceptions. Some tired old companies do turn vibrant. And there are well-told stories about how and why old struggling companies have beat the odds and changed their cultures, practices, and products for the better - although it is important to remember that nothing life is permanent, so such successes are best viewed as temporary and precarious.My favorite such stories include Lou Gerstner's Who Says Elephants Can't Dance, which I riff on for the title of this piece. Gerstner details how he led IBM's turnaround when innovation was stalled and the collective energy of the company was focused on politics, in-fighting, and preservation of outdated traditions rather than excellence. And IBM customers were routinely confused and neglected by the company. Creativity INC describes how, after Steve Jobs sold Pixar to Disney, President Ed Catmull and others from Pixar revitalized the spirit, confidence, and storytelling at the iconic but then struggling Disney Animation Studios. And one of the best such tales is James Surowiecki's 1998 New Yorker piece "The Billion Dollar Blade." It tells how a group of insiders at Gillette banded together to oust leaders who were leading the company into "commodity hell" and returned to Gillette's roots as a product innovator.I have a new candidate for anyone intrigued with the nitty-gritty of instilling innovation at scale: Michael Arena's new book Adaptive Space. I read an advance copy several months ago and was taken with the instructive blend of theory and research (especially on social network theory and innovation), stories about GM and other companies, and practical advice about what actually works. The book is compelling and fun to read, and accomplishes this without a hint of breathless hype or exaggeration.Many Silicon Valley companies that were once cute smart little startups but are turning into big dumb companies could a learn a lot from from Adaptive Space (including Tesla). As Michael shows, making innovation happen in a big company is a lot different than in little one. Michael's book will be released tommorow and we dropped our Stanford ecorner FRICTION podcast with Michael yesterday - which we titled "Agile on Edges: Managing Misfits." (You can listen to it, or if you prefer, read the transcript).I can't quite believe that I am praising book written by a GM executive. A decade ago, I was convinced that GM was doomed because it had a broken culture (based on frequent direct and indirect interactions with the firm's managers and executives). In 2008, I wrote a very critical post about the company that argued GM's core competence was captured by the phrase "No We Can't" - GM managers were the most skilled people I had ever met at explaining why, although they knew better ways to do things, it wasn't a good idea for GM to do them. They were a perfect illustration of The Knowing-Doing Gap, which Jeff Pfeffer and I wrote about back in 2000. And you likely recall that the company did, in fact, did go through Chapter 11 Bankruptcy in 2009 and was bailed out by the U.S. Government.What a difference a decade makes. GM paid back the money. Under CEO Mary Barra'sleadership, GM is financially healthy (some analysts make the case that the stock market undervalues GM, especially compared to Tesla). And, based on my admittedly biased view, the "no we can't" mindset is fading fast and innovation is evident in more and more GM people, practices, and products.The beauty of Michael's book - and our conversation on the FRICTION podcast - is that he digs into powerful nuances the propel innovation in big companies. He has much insight into how to dampen and overcome bad friction in big companies like General Motors, and about when friction is useful too - including resistance to new ideas, conflict over how promising new ideas should be realized, and careful (and sometimes slow) development of promising ideas before they are implemented at scale. He explains that, yes, some parts of big companies can and should be entrepreneurial, experimental, move fast, and do risky things; but it would be a disaster if everyone acted that way. Following work on the ambidextrous organization, he suggests that big companies must also simultaneously accomplish the routine, proven, and well-rehearsed stuff that makes money right now.I was taken with Michael's analogy that, to strike the right balance between scale and speed, he thinks of the core of a big company as much like a supertanker - where routine things happen, people have well-defined roles, and changes in direction are made with much forethought and unfold slowly. On the edges, however, are many speed boats, which move fast, travel to many new places, and try new things - all without affecting life on the supertanker. Many speed boats fail. Those that succeed get bigger and bigger, and when they become really successful, often come aboard and become part of the supertanker's operations.Michael's insights about how to manage the links between the supertanker and the speedboats are especially useful. Drawing heavily on social network theory, Michael suggests that, while having very smart people is important to innovation, more and more research suggests having the right blend of people and positions in the network, and creating the right connections between them, is the key to being a big innovative organization - for binding together what happens in the supertanker and in the speed boats. For example, he talks a lot about challengers, people who "break through the current status quo," and "see a different set of possibilities" The key, however, is that constructive challenges aren't just complainers and critics - they don't just annoy and distract their colleagues, and thus create dysfunctional friction. Instead, "they help break down the brick wall or pull other people and their ideas through the brick wall so that it can become the new big idea." And, as Michael added, they either have solutions to problems they complain about or ideas about how to develop solutions.Our interview and Adaptive Space unpacks the different kinds of roles and people that work together to bring new ideas into the core of social networks. Michael pointed out that "ideas developed inside small teams are 43% more likely to be rejected by the larger organization." But when new ideas are advanced by "energizers" - people who leave others feeling more motivated and enthusiastic about their work, themselves, and the organization - the newideas are far more likely be heard and spread. The implication, which has been around the innovation literature for a long time, is that the most successful innovators are adept at getting others excited about new ideas, about their roles in helping to develop and spread the ideas, and about selling the ideas to outsiders. Or if they are skilled at finding or inventing new ideas, but aren't adept energizers, they make innovation happen by teaming up with expert energizers. Steve Jobs and Thomas Edison were master energizers, but neither of those famous innovators had the best technical skills in their companies or industries. They become renowned innovators by teaming-up with more skilled inventors and technologists.I also like Michael's observation many of the best innovations already exist inside the organizations that need those ideas. He explains that social networks play a crucial role in finding and spreading these good but largely unknown and unused internal ideas. The role of "brokers" is key - these are people with connections to diverse people, groups, and ideas inside and outside of the organization. Because they have their fingers in so many different pies, brokers are often the first to learn about good ideas in their organizations and are in position to spread them to places where the ideas are not known or used. Michael says that brokers often uncover "positive deviance," pockets where great things are happening and that most of their colleagues don't know about. For example, Michael talks about a nurse at Einstein Medical Center in Philadelphia who knew about an area that had far lower rates of MRSA infections than elsewhere the hospital. The nurse attributed these lower rates to a janitor named Jasper Plummer. He taught doctors and nurses to remove their splattered surgical gowns in a way that sealed the soiled gowns in their surgical gloves. That method made his clean-up job easier and isolated the infection in the gloves. That nurse is a textbook example of a broker: Her connections to that unit meant she was one of the only a few people who knew about that practice and was also connected to the many other people and parts of the hospital who could benefit from using it - and thus Plummer's practice was spread it throughout the medical center.A final thought about Michael Arena's attitude and perspective. When we talked, Michael acknowledged my grumpy assertions that life in organizations is often messed up, frustrating, and exhausting. Yet he did not want to dwell on the causes and symptoms of dysfunctional friction that are rampant in nearly all big organizations. He wanted to talk about how to overcome and remove these and other obstacles to innovation - and he especially wanted to talk about the good things in organizations, and how networks enable people use their connections to find, develop, and scale good ideas. Michael's Adaptive Space, Lou Gerstner's Who Says Elephant's Can't Dance, and Ed Catmull's Creativity INC differ in many ways. The authors of all three of these wonderful books, however, have the same perspective on what it takes to fix a big stalled company: You can't let the bad news and setbacks get to you down. Your job is to make things a little bit better each day. And there is always something constructive you can do to make that happen.This piece was first posted on Linkedin.I am a Stanford Professor who studies and writes about leadership, organizational change, and navigating organizational life. My latest book is The Asshole Survival Guide: How To Deal With People Who Treat You Like Dirt. Before that, I published Scaling Up Excellence with Huggy Rao. My main focus these days is on working with Huggy Rao to develop strategies and tools that help leaders and teams change their organizations for the better - with a particular focus onorganizational friction. Check out my Stanford "FRICTION Podcast" at iTunes or Sticher·RELATED QUESTIONWhat is the working principle of an electric motor ?An electric motor uses the attraction and repulsion of magnetic fields to produce motion. The simplest is the permanent magnet motor. A simplified version is shown below.Placing a coil of wire inside a permanent magnetic field and fixed so it can freely rotate. Pass a current through the coil of wire and it will rotate to the perpendicular position. Now reverse the current flow and the coil will spin 180 degrees. The brushes and the commutator does the switching directions of the current through the coil at the appropriate moment to keep the coil rotating in one direction. This is the basics of all electric motors.Fleming's left hand rule above describes the relationship between the main magnetic field. The current flowing in the coil and the direction of the movement or forceNow we can use electronics to do the switching instead of the commutator and brushes. These motors are called Brushless DC motors.Larger motors require a stronger magnetic field and more electrical ,power to drive it faster and with more torque. Stronger magnetic fields are created by electromagnets.AC motors use induction from the stationary windings to create the second magnetic field in the rotor. That induces currents in the rotor and these currents have their own magnetic field which interact ( repel and attract) with the main magnetic field to make the rotor rotate
What Is a Disable Input for on a Motor Controller For?
PWM Controlled MOSFET Based DC Motor Driver, Stuck with Reverseing Direction
Electric Bikes for Sale at Best Buy - Cbs News
Steps in Rewinding Three Phase Motor?
CBT: I've Never Riden a Bicycle in My Life Or a Motorcycle?
Where Is the Best Place to Buy Motorcycle Parts Online?
Five Formula 1 Cars for the Road
How to Shifting Gears on a Motorcycle?
Which Motorcycle Brand You Own and Why?
related searches
Yes, That Elephant Can Dance: General Motors Chief Talent Officer on Innovation at Scale
What Is a Disable Input for on a Motor Controller For?
Advanced Materials for Electric Motor and Generator Windings
How to Use a Trolling Motor with a Foot Pedal Like a Pro
Whats the Difference Beetween a Motor and an Engine?
Using Capacitors to Power a Motor?
High Level Vs Low Level Motor Control - Acceleration and Velocity Profiles
What Is the Maximum Current at Which I Can Drive My Stepper Motor?
How Does a Multi-tap Motor Speed Control Work?

Copyright © 2020  Shandong Abusair Agricultural Machinery Co,. Ltd- |  Sitemap

Multifunctional farm Abusair machinery  |  Tea Professional Cultivator farm machinery