Praxis Dynamics  >>  Services

State of the Art Survey
We offer State of the Art Surveys that enable the client to position the product or the technology in its larger scientific and technological context. By removing the clutter of scientific papers, patents and reports, the survey presents a comprehensive and quantitative tool for business and research planning. A clear picture of novel capabilities, the current scientific state of the art and the relevant intellectual property provides for an informed decision making. In the process, further improvements based on cross-domain technological analysis are often identified by us.    Key Deliverables
  • State of the Art Survey.
  • Independent critical appraisal of scientific materials.
  • Analysis of the scientific and commercial context.
  • Comparative review of new capabilities.
  • Identification of possible improvements based on cross-domain analysis.
  • Comprehensive list of relevant published materials.
Cross-domain Physical Modelling
A complete mathematical model of the micro-system is developed, based on a comprehensive analysis across all relevant physical domains. The model is instrumental in defining the design parameter space and setting realistic performance goals. A sophisticated mathematical analysis of the model reveals all important behavioural features and stability issues. The final product is a tractable and computationally feasible model as required for engineering analysis, design and simulation.    Key Deliverables
  • Cross-domain physical modelling and analysis.
  • Combining first principles, empirical data and physical insight.
  • Identifying and reliably representing all important features of the system.
  • Tractable and computationally feasible model.
  • Full documentation.
Nonlinear Analysis
Combined analytical and numerical analysis of nonlinear models provides a valuable insight into their nontrivial dynamics and possible stability issues. Based on this comprehensive analysis, the initial mathematical complexity may be reduced to create a manageable simulation model, while maintaining a reliable representation of all important dynamical features and stability thresholds.    Key Deliverables
  • Analytical approximations and bifurcation analysis.
  • Stability analysis and threshold identification.
  • Model complexity reduction preserving the essential features.
  • Numerical simulations and model validation.
Simulation Model Development
The reliability of a simulation model can be assured only if the underlying physical processes are well understood. We take full advantage of our abilities to perform mathematical and physical analysis of complex systems in order to create efficient and comprehensive behavioural simulation models. Our main focus is on Matlab and SPICE simulations, where our in-depth acquaintance with the internal algorithms and general analogue computing techniques contribute to further model optimization and refinement.    Key Deliverables
  • Optimization of mathematical models for numerical simulations.
  • Accurate representation of the dynamics and stability thresholds.
  • Reduced order models.
  • Custom SPICE models and equivalent circuits.
  • Full documentation.
Parameter Extraction
Extraction of meaningful and reliable values for different physical parameters from experimental results requires excellent grasp of the theoretical model and the capabilities of the available experimental methods. Using theory and numerical simulations, we are able to estimate the importnant physical parameters of the system, including electrical, mechanical, optical and thermal properties. Utilising this knowledge, an effective and comprehensive measurement plan is devised, incorporating effective algorithms for direct and indirect measurements. Our goal is to extract maximum information even from limited experimental data. The results always undergo a sanity check based on first principles and theoretical expectations.    Key Deliverables
  • Theoretical and numerical evaluation of physical properties.
  • Effective and comprehensive measurement plan.
  • Maximum utilisation of experimental data for parameter extraction.
  • Sanity check based on theoretical expectations