In nonlinear transient analysis, the gold standard for solving the impossible is to use the explicit method but this often limits the solution time to milliseconds and rather large elements (see our LS-DYNA Class Notes). To solve nonlinear problems where the solution is in seconds or static, the implicit technique offers many advantages albeit with other challenges. Over the last several years, our simulation engineers have tackled a number of nonlinear static and transient problems using the implicit method. The choice of implicit over explicit has allowed us to solve nonlinear FEA problems that heretofore were just not practical in the sense of time and budget.
Most of our clients could care less whether the analysis results were obtained using an implicit or explicit numerical technique, just so long as it was quick and accurate. On our side, deep within the numerical salt mines, we do care whether it is implicit or explicit since picking the wrong numerical path can mean the difference between success or one pissed off client.
This project was our most challenging nonlinear transient implicit analysis in our history. Thanks to our client we were able to present this work at a conference (see 032_Jensen, A. - Transient Dynamic Implicit Analysis for Durability Testing of Bus Seats.pdf). Experimental test track data was harvested as accelerations and then converted to displacement load data. These loading curves were then used to drive the FEA model over time periods from five to ten seconds. The model employed contact between components, bolt preload and material yielding throughout the transient loading event. If we had tried to run this model using the explicit method, each analysis would have taken weeks whereas the implicit method generated a complete run in more or less six hours on 32 CPU-Cores.
A nonlinear transient implicit analysis was done to determine the anchor loading during 9g cargo net deployment. Four nets were idealized and simulated under 9g deployment: i) Main Cargo Net; ii) Separator Net, iii) Aft Cargo Net and iv) Front Dogleg Cargo Net. Our client used the anchor loads calculated from the analysis work to design the frame attachment structures. Being able to run the models in implicit provided a bit more flexibility and speed (5x faster) but was not really necessary since we also verified the models against an explicit run.
At Predictive Engineering, we are generalists with simulation skill sets from submarines, medical instruments, FFG(X) ships to aviation and space. As part of this portfolio work, we occasionally get involved with ITAR work. This project started with the development of the composite material models via validation of FEA models of tensile and 4-Pt bend test coupons. With this foundation, the composite aerospace structure was analyzed under transient dynamic loading using the implicit method. Why implicit? The loading regime was in the order of 50 of milliseconds and it wasn’t necessary to capture frequencies above 500 Hz, hence, we could use an implicit time step of 0.2 millisecond and have a solution with 250 steps and a quick solve time. The results compared favorable against the experimental tests.
This project was driven by the desire to create more efficient energy absorbing structures for military helmet liners. The idea is to custom design the energy absorbing response by unique lattice structures that could only be created by additive manufacturing. The lattice dimensions were in the order of millimeters and were meshed using tetrahedrals. With such small elements, the only efficient analysis technique was implicit. The US Army chose us for this project due to our prior published work on nonlinear FEA modeling.
This FEA consulting project was done for an off-shore transit car manufacturer. The idea was to demonstration that one FEA model could be used to analysis static loads (implicit) according to ASME RT-2-2014 and also for crash loading (explicit).
Our client came to us after one of their camera housings had failed during hydrostatic testing. We had worked with this company for many years performing various types of FEA services on marine winches, tow vehicles and other ship mounted structures. This project was unique since our analysis work showed that the housing should not have failed during service. To explore the robustness of the design, we loaded the housing to collapse. Results demonstrated that the failure had to be due to a metallurgical defect. Upon further inspection, it was discovered that machining had created an overly sharp notch and slightly “burned” the material creating a crack initiation site that led to the failure of the housing. A slight modification was made to the machining procedure and all subsequent housings passed the hydrostatic test.
Predictive Engineering FEA and CFD Engineering Services
Understand, you have been reading this far, you sort of know by now our business game but I have been surprised too many times by clients asking us if we do CFD (a FEA client), etc.
Spring design is a very classic mechanical engineering task and usually one can get obtain a reasonable solution following handbook rules. In this case, our client came to us after repeated fatigue failures of their high-tensile strength extension spring. Their design required the use of an extension spring (a spring that is cold rolled to create an initial preload state that requires a certain force to initial extension). The analysis technique required us to preload the spring (beam elements) and then use this stress state as an initial stress for the subsequent device movement. Analysis results showed that fatigue damage was real and that their only design solution was to lower the extension spring preload.
General Examples of Implicit Nonlinear and Transient Dynamic FEA Consulting Projects from composite engineering, rubber seal analysis, ASME Section VIII, Division 2 "Design-by-Analysis", Aviation Seat 16-g Sled Test Analysis to Medical Equipment for Endoscopic Surgery (wire cable analysis).
Predictive Engineering FEA and CFD Consulting Services - Portland, Oregon with over 25 years of verified and validated simulation experience.