MSC Adams
View on WikipediaMSC ADAMS (Automated Dynamic Analysis of Mechanical Systems) is a multibody dynamics simulation software system. It is currently owned by MSC Software Corporation. The simulation software solver runs mainly on Fortran and more recently C++ as well.[1] According to the publisher, Adams is the most widely used multibody dynamics simulation software.[2] The software package runs on both Windows and Linux.
Capabilities
[edit]Adams has a full graphical user interface to model the entire mechanical assembly in a single window. Graphical computer-aided design tools are used to insert a model of a mechanical system in three-dimensional space or import geometry files such as STEP or IGS. Joints can be added between any two bodies to constrain their motion. Variety of inputs such as velocities, forces, and initial conditions can be added to the system.
Adams simulates the behavior of the system over time and can animate its motion and compute properties such as accelerations, forces, etc. The system can include further complicated dynamic elements like springs, friction, flexible bodies, and contact between bodies.[2] The software also provides extra CAE tools such as design exploration and optimization based on selected parameters. The inputs and outputs of the simulation can be interfaced with Simulink for applications such as control.
Applications
[edit]The Adams software package is used both in academic research and engineering. The most common usage of the software is analysis of vehicle structure and suspension through the Adams/Car and Adams/Tire modules.[3][4][5] Various types of mechanical systems such as wind turbines,[6] powertrains,[7] and robotic systems.[8]
References
[edit]- ^ Ortiz, Jose (May 18, 2011). "Introduction to Adams/Solver C++" (PDF). mscsoftware.com. Retrieved June 2, 2020.[permanent dead link]
- ^ a b "Adams Real Dynamics for Functional Virtual Prototyping" (PDF). MSC Software. September 2013. Retrieved June 2, 2020.
- ^ Jadav, Chetan S., and Jignesh R. Gautam. "Multibody Dynamic Analysis of The Suspension System Using Adams." International Journal for Scientific Research & Development 2.03 (2014): pp.
- ^ Li, Sheng-qin, and Le He. "Co-simulation study of vehicle ESP system based on ADAMS and MATLAB." Journal of Software 6.5 (2011): 866-872.
- ^ Burdzik, R., and B. Łazarz. "Analysis of properties of automotive vehicle suspension arm depending on different materials used in the MSC. Adams environment." Archives of Materials Science and Engineering 58.2 (2012): 171-176.
- ^ Zierath, János, Roman Rachholz, and Christoph Woernle. "Field test validation of Flex5, MSC. Adams, alaska/Wind and SIMPACK for load calculations on wind turbines." Wind Energy 19.7 (2016): 1201-1222.
- ^ Peicheng, Shi, Chen Wuwei, and Chen Liqing. "Study on Vibration Isolation Characteristics of Automobile Powertrain Mount System Based on Co-simulation." Transactions of the Chinese Society for Agricultural Machinery 41.2 (2010): 29.
- ^ Cheraghpour, Farzad, et al. "Dynamic modeling and kinematic simulation of Stäubli© TX40 robot using MATLAB/ADAMS co-simulation." 2011 IEEE International Conference on Mechatronics. IEEE, 2011.
MSC Adams
View on GrokipediaHistory
Origins at University of Michigan
Research on multibody system dynamics at the University of Michigan was initiated in 1967 by Professor Milton A. Chace, who focused on developing computational methods for analyzing complex mechanical systems. Chace's work built on his earlier Ph.D. research in vector mathematics for kinematic analysis, aiming to create generalized computer representations of multifreedom, constrained mechanical systems using network-variational approaches. This foundational effort addressed the challenges of simulating time-dependent behaviors in relative coordinates, marking a shift toward automated tools for engineering design.[7][8] By 1969, Chace and his collaborators had advanced early algorithms for three-dimensional multibody simulation, incorporating sparsity-oriented techniques to handle large-scale systems efficiently. These developments emphasized kinematic and dynamic analyses, particularly for intricate applications such as aircraft landing gear, where precise modeling of interactions under load was critical. The research laid the groundwork for handling nonlinear constraints and forces in mechanical networks, influencing subsequent academic and industrial simulations.[7][8] A pivotal contribution came from Nicolae Orlandea, who joined the University of Michigan in 1970 under Chace's supervision and completed his Ph.D. in 1973. Orlandea's doctoral thesis, titled "Node Analogous, Sparsity Oriented Methods for Simulation of Mechanical Dynamic Systems," introduced innovative sparse tableaux formulations and integration methods, such as the Gear algorithm, for automated dynamic analysis. This work formed the core of the ADAMS (Automatic Dynamic Analysis of Mechanical Systems) solver, enabling efficient three-dimensional simulations of multibody dynamics with a focus on real-world engineering problems like landing gear retraction and deployment. Orlandea, recognized as the original architect of ADAMS, passed away in 2023.[9][10][11]Commercialization by Mechanical Dynamics Corporation
Mechanical Dynamics Corporation (MDI), originally known as Mechanical Dynamics, Inc., was founded in 1977 by University of Michigan alumni, including Milton Chace and Mike Korybalski, to commercialize multibody dynamics simulation technologies emerging from academic research, with significant contributions from Nicolae Orlandea, the original architect of ADAMS.[12][13][9] The company adopted ADAMS as its flagship product in 1978, building on Orlandea's doctoral work from the early 1970s.[8] The first commercial version of ADAMS was released in 1981, marking its transition from a research tool distributed freely through NASA's COSMIC network to a proprietary multibody dynamics software for engineering simulations on mainframe and early workstation computers.[8][9] This release emphasized automated analysis of mechanical systems, enabling users to model complex assemblies with joints, forces, and constraints without extensive manual equation derivation. By the mid-1980s, following the end of non-commercial distribution in 1984, ADAMS gained traction as a standard for virtual prototyping, reducing the need for physical testing in design cycles.[9] Early adoption was prominent in the automotive and aerospace sectors, where ADAMS facilitated simulations of vehicle suspensions and aircraft components; notable examples include an SAE collaborative project analyzing the Chevrolet Malibu's suspension dynamics in the late 1970s and Boeing's evaluation of 747 landing gear behavior.[1][9] These applications demonstrated ADAMS's value in predicting system performance under real-world loads, accelerating development timelines and cost savings for major manufacturers.[1] Throughout the 1980s, MDI focused on enhancements driven by user feedback, including refinements to solver algorithms for greater efficiency in rigid body dynamics simulations and the gradual introduction of graphical user interfaces to simplify model building and visualization.[9] A pivotal advancement was the integration of flexible body modeling in the mid-1980s, based on component mode synthesis techniques, which expanded ADAMS's applicability to durability and vibration analyses beyond purely rigid mechanisms.[9] These improvements solidified ADAMS's position as a leading tool for multibody system analysis during the decade.Acquisition by MSC Software and Later Developments
In March 2002, MSC Software Corporation acquired Mechanical Dynamics Inc., the developer of the ADAMS multibody dynamics software, for approximately $128 million in a cash tender offer and subsequent merger. This acquisition expanded MSC Software's portfolio in simulation tools and integrated ADAMS into its broader computer-aided engineering (CAE) ecosystem, serving over 10,000 customers globally.[2] Following the acquisition, the software was rebranded as MSC.ADAMS, emphasizing its alignment with MSC's suite of analysis products. A key enhancement came with the release of MSC.ADAMS 2003 in July 2003, which improved integration for flexible body simulations by enabling easier generation of flexible bodies directly from MSC.Nastran models, including pre-stress effects for more accurate dynamic analyses. This version also introduced advanced 3D contact modeling using faceted geometry for faster and more realistic simulations of part interactions.[14] In February 2017, Hexagon AB acquired MSC Software for $834 million on a cash and debt-free basis, with the transaction completing in April 2017. Under Hexagon's ownership, MSC.ADAMS continued to evolve as part of the Manufacturing Intelligence division, benefiting from broader resources for multiphysics and digital twin applications.[3] As of 2025, developments under Hexagon include the expansion of Adams Real Time, a hardware-in-the-loop (HIL) solution that enables software-in-the-loop (SIL), HIL, and advanced driver assistance systems (ADAS) co-simulations to reduce physical prototyping needs by emulating real-time vehicle dynamics. Additionally, the introduction of Adams Car On Demand in August 2025 provides scalable cloud-based simulation capabilities, allowing automotive engineers to run full vehicle dynamics analyses remotely and accelerate design iterations without local high-performance computing resources. In September 2025, Hexagon announced an agreement to sell its Design & Engineering division, including MSC Software, to Cadence Design Systems for €2.7 billion, with the transaction expected to close in the first quarter of 2026.[15][16][4]Technical Overview
Core Architecture and Programming
MSC Adams employs a hybrid programming approach, with its numerical solvers primarily implemented in Fortran for legacy compatibility and core computational efficiency, while the modern Adams Solver version is written in C++ to enhance performance, robustness, and integration with contemporary software ecosystems. The C++ solver is the default across the Adams product line and is recommended for its superior speed in handling complex multibody dynamics simulations. This dual-language strategy leverages Fortran's strengths in numerical stability for traditional solver routines alongside C++'s object-oriented capabilities for optimized execution.[17][18] The software's core architecture is modular, enabling users to assemble tailored workflows by combining solver engines, preprocessing tools, and postprocessing modules into cohesive simulation environments. This design facilitates scalability, from standalone analyses to enterprise-level integrations, and supports deployment on both Windows and Linux operating systems, including distributions such as Red Hat Enterprise Linux 7.x and later. The modular structure ensures portability across platforms while maintaining consistency in solver behavior and data flow.[19][20] Integration with external tools is a key aspect of Adams' architecture, particularly through co-simulation capabilities with environments like MATLAB and Simulink via the Adams/Controls module. This allows seamless exchange of mechanical system models and control algorithms, where Adams handles multibody dynamics and Simulink manages signal-based controls, enabling iterative design of hybrid systems without custom bridging code. Such interoperability is supported through standardized interfaces that synchronize simulation steps and data transfer in real time.[21][22] For data handling, Adams supports robust import and export of 3D models in standard CAD formats, including STEP (ISO 10303) and IGES, to facilitate geometry transfer from design tools like CATIA or SolidWorks. This capability ensures accurate representation of parametric surfaces, solids, and assemblies in multibody models, with options for tessellation or direct kernel-based translation to preserve fidelity during preprocessing. Export functions similarly allow results to be shared back to CAD systems for design refinements.[23][24]Simulation Solvers and Methods
MSC Adams employs Lagrange multipliers to enforce constraint equations in multibody dynamics simulations, formulating the system as a set of differential-algebraic equations (DAEs) of index 3. This approach incorporates the Lagrangian of the system, where the equations of motion are augmented with constraint terms:with representing holonomic constraints, the constraint Jacobian, and the vector of Lagrange multipliers that compute constraint reaction forces.[25] This method ensures accurate handling of joints, motions, and other kinematic constraints in both kinematic and dynamic analyses.[25] For kinematic analyses, which solve for positions and velocities without inertial forces, MSC Adams uses Newton-Raphson iterations to resolve nonlinear constraint equations, typically converging within 25 iterations at an error tolerance of .[25] Dynamic analyses, incorporating forces and accelerations, rely on implicit integrators to solve the resulting DAEs, with the Adams Solver (implemented in C++ for enhanced performance) supporting modes for statics, quasi-statics, and full transients.[25] Key implicit methods include GSTIFF, a variable-step, variable-order backward differentiation formula (BDF) integrator up to sixth order, optimized for stiff systems by controlling displacement and velocity errors (default tolerance ); HHT, which applies a low-pass filter with to damp high-frequency oscillations; and variants like WSTIFF and Newmark (, ).[25] Explicit methods, such as ABAM (up to 12th order Adams-Bashforth-Moulton) and RKF45 (Runge-Kutta-Fehlberg), are available for non-stiff problems but are less common due to stability limitations in multibody contexts.[25] These integrators, often paired with stabilized index-2 (SI2) formulations, allow larger tolerances for velocity and acceleration solutions while maintaining constraint drift control.[25] Contact interactions in MSC Adams are managed through specialized algorithms that model impact and friction without requiring explicit geometric meshing. Impact forces follow nonlinear spring-damper models, such as the IMPACT function (where is penetration depth, stiffness, exponent typically 2.2, and damping), or the Poisson model (with impulse factor and restitution coefficient).[25] Friction employs a Coulomb model distinguishing static () and dynamic () coefficients, handling stiction, sliding, and transitions via velocity-based transitions and torque calculations like for rotational cases.[25] Augmented Lagrangian or penalty methods augment these for better convergence, reducing penetration errors in stiff contacts.[25] Flexible bodies are integrated using modal methods, where finite element models are reduced to modal neutral files (MNF) containing mode shapes and frequencies. The Craig-Bampton component mode synthesis transforms the system into modal coordinates , yielding equations like , with damping via critical damping ratios (CRATIO) up to specified frequencies (FXFREQ).[25] The FLEX_BODY statement specifies active modes (NMODES) and load matrices, enabling linear or nonlinear flexible elements in contact with rigid bodies while preserving computational efficiency.[25] Optimization routines in MSC Adams target design parameters by minimizing objectives subject to constraints, leveraging both gradient-based and stochastic approaches. Gradient-based methods include sequential quadratic programming (SQP) for faster convergence on smooth problems, modified method of feasible directions (MMFD) for robustness, sequential linear programming (SLP), and sequential unconstrained minimization technique (SUMT).[26] For global search, the stochastic design improvement (SDI) algorithm, akin to genetic methods, iteratively generates random trial designs around current points and selects improvements based on fitness.[26] Multi-objective optimization uses cost functions like total deviation or worst-case minimization, with adjustable tolerances and iteration limits, often integrated with design of experiments (DOE) via response surface models for efficient parameter tuning.[26]