When Lam and her students build a model of a production line or an entire system, they’re able to see where there are bottlenecks and idle resources and even where there are activities that aren’t adding value. Such simulations often lead to a company relocating materials or reallocating operators to reduce travel time or material-handling time as well as to new designs for product flow and facility layout. “Activities that don’t add value should be eliminated,” she says. “Inventory should be minimized. We want things to move smoothly and quickly through the system.”

This kind of simulation is especially good for exploring what-if scenarios, says Lam, an associate professor of systems science and industrial engineering at Binghamton. “Building an imitation of how a system and its components work so you can actually see things moving adds depth to the planning stage,” she notes. “That lets you test out an idea and see if you get the benefits you expected. You see what isn’t going to work and how deadlines will be affected. Then you can make changes in the simulation world to see if you can get better results.”

Lam’s simulations allow her to compress time, too. Once a model has been built, using time studies and historical data as well as the relevant physical details, she can simulate how a system will work over the course of a year in just minutes. “Simulation results not only can assist with planning but also often can tell us something new,” she says.  “We can see results faster.”

Visuals, even a simple 2D model, can make a huge difference and are increasingly common in the software Lam and her students use. In fact, newer software packages often include 3D modeling. Such simulations help people visualize the entire system and focus on what’s moving, whether it’s patients moving through a hospital’s emergency room or a printed circuit board moving through an electronics manufacturing line.  “Companies like to see animation,”  Lam says.  “They say,  ‘Show me how it runs.’ Animation is a big deal for communicating.”

She and her students can create simulations in as little as a couple of weeks or, for more complex models, in as much as several months. A typical project takes two to nine months.

“It allows companies to see what they’re not doing so well and where they can improve,” she says. “The bottom line is important. We provide the tools to help them get there.”

Lam’s primary motivation is in seeing a system work more smoothly. “As industrial engineers, we don’t make things, but we try to improve on how things are done,” she says. “Seeing an improvement is gratifying. We have seen significant improvement in return on investment. That’s very important. That’s one of the ways we can maintain the research relationship year after year.”

Cardullo, then a Link Simulation and Training employee, worked on the Apollo program and has decades of experience in simulation for flight and aerospace applications. Since joining Binghamton’s faculty in 1980, he has explored some of the underlying principles of simulation, including mathematical models and signal processing.

“Space simulators are unique, particularly the Apollo simulators,” Cardullo says. “You can train people to fly airplanes and drive cars by having them fly airplanes and drive cars. But you couldn’t train an astronaut to go to the moon by going to the moon. So you had to have a very sophisticated simulator to do that. The astronauts often said ‘just like in the simulator’ as they encountered events.”

In recent years, flight and driving simulators have grown increasingly realistic, with significant improvements in motion cues. Cardullo notes that physiology and biology play a growing role in simulation, as do new ideas about cognition and learning.

“Simulators, I think, are creating better pilots,” he says. “They’re better able to handle complex situations.” Engine failure, like the problem in the 2009 so-called Miracle on the Hudson flight, is a prime example. Most pilots experience engine failure only in simulations. Nevertheless, they are often able to identify it and to respond appropriately because of that training.

Cardullo, a professor of mechanical engineering whose work has been supported by NASA as well as the Office of Naval Research and the Air Force Research Laboratory, earned his master’s degree from Binghamton. He sees applications for his work wherever he goes. A decade ago, he explored the possibility that data from simulators could be used to identify specific pilots, which could be useful in determining whether a pilot had been disabled or was impaired in some way. An avid Red Sox fan, he has also considered taking what he knows about brain waves to see whether they are the reason that some athletes respond faster than others to visual information. Perhaps, he says, there was some scientific truth behind the  “fast eyes” of the late Boston great Ted Williams.

Cardullo is also interested in medical applications of his field, especially in the idea that surgeons benefit from simulation training in much the same way pilots do. Simulators can present malfunctions and difficulties to doctors so that they can become more experienced in dealing with them. (Standard training now often relies on the use of pigs.) Cardullo hopes to build a simulator that would provide tactile sensation to surgeons.  “My hypothesis is that tactile feedback will improve the surgeon’s performance,” he says. Simulation technology may also lead to improvements in the design of surgical robots, Cardullo says, just as it has led to better aircraft.

He wants to examine the complications surrounding robotic surgery done remotely, including questions about satellite and phone connections. “The up-down time can be two seconds,”  he says.  “A lot of simulation research that I’ve done over the years is looking at the effect of that communication delay on human performance. Once you get over about 70 or 80 milliseconds of delay, it affects performance. You can’t have that if somebody’s cutting out someone’s liver.”

In the particular condition that Beaumont studies, inherited arrhythmia, scientists know that some three dozen proteins play a role in the defect. Simulation enables Beaumont to test potential procedures in a model pig’s heart before attempting to extrapolate the results for the human heart.

“Naturally, you cannot do experiments in the human heart,” says Beaumont, an associate professor of bioengineering at Binghamton University. “An animal model is not the same as a human’s, but the animals that are closest are the pig and the dog. What we try to do is take advantage of the data gathered from an animal model. We can build a simulation that parallels the animal model and then validate that our procedure works.”

Beaumont creates visual models with underlying mathematical models in his search for the mechanism of arrhythmia. He hopes to link the molecular aspect of cellular excitation to the phenomenon at the microscopic level. The simulation doesn’t yet include the application of possible therapies, though that’s a dream of his.

Scientists can identify those at high risk for arrhythmia, but they don’t know how it occurs or what triggers it. “It’s almost impossible to study this solely on the basis of experimentation,” Beaumont says. “Simulation is very useful. We have tremendous technology at our disposal in bioengineering. Among other things, we can get imaging of tissue and organs at high resolution and even map the distribution of protein expression. The decoding of the genome is allowing us to easily determine whether an individual is harboring defective proteins. All of this constitutes a massive amount of information. The future of medicine lies in better ways to integrate and exploit this data to develop cures.”

In cardiac modeling, there are many interrelated bits of data involved in the computation and differential equations to be solved along the way. That’s why — even with a supercomputer — it can take one to four days to run one of Beaumont’s simulations. “The exchange of information during computation is like circulation on a very congested highway,” he says. “If you have a bottleneck somewhere, a lot of other things are affected and it becomes hard to control traffic.”

He compares the moment that a simulation delivers a particularly surprising or amazing result to his childhood experiences of athletic victory.  “You are traversed by a wave of satisfaction from toe to head,” he says, grinning.

Beaumont says he and his colleagues are motivated by an opportunity to save lives, to put their model into the hands of clinicians and develop a therapy. “There is,” he says, “no better way to study a multi-scale problem like this.”