Using CAD parameter sensitivities for stack-up tolerance allocation

Tolerance allocation is an important step in the design process. It is necessary to produce high quality components cost-effectively. However, the process of allocating tolerances can be time consuming and difficult, especially for complex models. This work demonstrates a novel CAD based approach, where the sensitivities of product dimensions to changes in the values of the feature parameters in the CAD model are computed. These are used to automatically establish the assembly response function for the product. This information has been used to automatically allocate tolerances to individual part dimensions to achieve specified tolerances on the assembly dimensions, even for tolerance allocation in more than one direction simultaneously. It is also shown how pre-existing constraints on some of the part dimensions can be represented and how situations can be identified where the required tolerance allocation is not achievable. A methodology is also presented that uses the same information to model a component with different amounts of dimensional variation to simulate the effects of tolerance stack-up. © 2014 Springer-Verlag France.

Characterisation of Passive Intermodulation in Passive RF Devices with X-parameter

A novel approach to the modelling of passive intermodulation (PIM) generation in passive components with distributed weak nonlinearities is outlined. Based upon the formalism of X-parameters, it provides a unified framework for co-design of antenna beamforming networks, filters, combiners, phase shifters and other passive and active devices containing nonlinearities at RF front-end. The effects of discontinuities and complex circuit layouts can be efficiently evaluated with the aid of the equivalent networks of the canonical nonlinear elements. The main concepts are illustrated by examples of numerical simulations of PIM generation in the transmission lines and comparison with the measurement results.

Improving parameter learning of Bayesian nets from incomplete data

This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy approach and a Bayesian model averaging approach. Both ideas can be easily applied on top of EM, while the entropy idea can be also implemented in a more sophisticated way, through a dedicated non-linear solver. A vast set of experiments shows that these ideas produce significantly better estimates and inferences than the traditional and widely used maximum (penalized) log-likelihood and maximum a posteriori estimates. In particular, if EM is adopted as optimization engine, the model averaging approach is the best performing one; its performance is matched by the entropy approach when implemented using the non-linear solver. The results suggest that the applicability of these ideas is immediate (they are easy to implement and to integrate in currently available inference engines) and that they constitute a better way to learn Bayesian network parameters.

Cavity-aided quantum parameter estimation in a bosonic double-well Josephson junction

We describe an apparatus designed to make non-demolition measurements on a Bose-Einstein condensate (BEC) trapped in a double-well optical cavity. This apparatus contains, as well as the bosonic gas and the trap, an optical cavity. We show how the interaction between the light and the atoms, under appropriate conditions, can allow for a weakly disturbing yet highly precise measurement of the population imbalance between the two wells and its variance. We show that the setting is well suited for the implementation of quantum-limited estimation strategies for the inference of the key parameters defining the evolution of the atomic system and based on measurements performed on the cavity field. This would enable {\it de facto} Hamiltonian diagnosis via a highly controllable quantum probe.

Electrostatic Solitary Waves in Relativistic Degenerate Electron-Positron-Ion Plasma

The linear and nonlinear properties of ion acoustic excitations propagating in warm dense electron-positron-ion plasma are investigated. Electrons and positrons are assumed relativistic and degenerate, following the Fermi-Dirac statistics, whereas the warm ions are described by a set of classical fluid equations. A linear dispersion relation is derived in the linear approximation. Adopting a reductive perturbation method, the Korteweg-de Vries equation is derived, which admits a localized wave solution in the form of a small-amplitude weakly super-acoustic pulse-shaped soliton. The analysis is extended to account for arbitrary amplitude solitary waves, by deriving a pseudoenergy-balance like equation, involving a Sagdeev-type pseudopotential. It is shown that the two approaches agree exactly in the small-amplitude weakly super-acoustic limit. The range of allowed values of the pulse soliton speed (Mach number), wherein solitary waves may exist, is determined. The effects of the key plasma configuration parameters, namely, the electron relativistic degeneracy parameter, the ion (thermal)-to-the electron (Fermi) temperature ratio, and the positron-to-electron density ratio, on the soliton characteristics and existence domain, are studied in detail. Our results aim at elucidating the characteristics of ion acoustic excitations in relativistic degenerate plasmas, e.g., in dense astrophysical objects, where degenerate electrons and positrons may occur.

Re-dispersion of gold supported on a ‘mixed’ oxide support

The ability to reactivate, stabilize and increase the lifetime of gold catalysts by dispersing large, inactive gold nanoparticles to smaller nanoparticles provides an opportunity to make gold catalysts more practical for industrial applications. Previously it has been demonstrated that mild treatment with iodomethane (CH3I) (J. Am. Chem. Soc., 2009, 131, 6973; Angew. Chem. Int. Ed., 2011, 50, 8912) was able to re-disperse gold on carbon and metal oxide supports. In the current work, we show that this technique can be applied to re-disperse gold on a ‘mixed’ metal oxide, namely a mechanical mixture of ceria, zirconia and titania. Characterization was conducted to gage the impact of the iodomethane (CH3I) treatment on a previously sintered catalyst.

Energy Saving - Another Perspective for Parameter Optimization of P and PI Controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

Mathematical models are useful tools for simulation, evaluation, optimal operation and control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the model parameters of these two type of cells efficiently, a biogeography-based optimization algorithm with mutation strategies (BBO-M) is proposed. The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Numerical experiments have been conducted on ten benchmark functions with 50 dimensions, and the results show that BBO-M can produce solutions of high quality and has fast convergence rate. Then, the proposed BBO-M is applied to the model parameter estimation of the two type of cells. The experimental results clearly demonstrate the power of the proposed BBO-M in estimating model parameters of both solar and fuel cells.

An Investigation into the Role of Polymeric Carriers on Crystal Growth within Amorphous Solid Dispersion Systems

Using phase diagrams derived from Flory–Huggins theory, we defined the thermodynamic state of amorphous felodipine within three different polymeric carriers. Variation in the solubility and miscibility of felodipine within different polymeric materials (using F–H theory) has been identified and used to select the most suitable polymeric carriers for the production of amorphous drug–polymer solid dispersions. With this information, amorphous felodipine solid dispersions were manufactured using three different polymeric materials (HPMCAS-HF, Soluplus, and PVPK15) at predefined drug loadings, and the crystal growth rates of felodipine from these solid dispersions were investigated. Crystallization of amorphous felodipine was studied using Raman spectral imaging and polarized light microscopy. Using this data, we examined the correlation among several characteristics of solid dispersions to the crystal growth rate of felodipine. An exponential relationship was found to exist between drug loading and crystal growth rate. Moreover, crystal growth within all selected amorphous drug–polymer solid dispersion systems were viscosity dependent (η–ξ). The exponent, ξ, was estimated to be 1.36 at a temperature of 80 °C. Values of ξ exceeding 1 may indicate strong viscosity dependent crystal growth in the amorphous drug–polymer solid dispersion systems. We argue that the elevated exponent value (ξ > 1) is a result of drug–polymer mixing which leads to a less fragile amorphous drug–polymer solid dispersion system. All systems investigated displayed an upper critical solution temperature, and the solid–liquid boundary was always higher than the spinodal decomposition curve. Furthermore, for PVP–FD amorphous dispersions at drug loadings exceeding 0.6 volume ratio, the mechanism of phase separation within the metastable zone was found to be driven by nucleation and growth rather than liquid–liquid separation.

Solubility parameter based screening methods for early stage formulation development of itraconazole amorphous solid dispersions

Objectives: This article uses conventional and newly extended solubility parameter (δ) methods to identify polymeric materials capable of forming amorphous dispersions with itraconazole (itz). Methods: Combinations of itz and Soluplus, Eudragit E PO (EPO), Kollidon 17PF (17PF) or Kollidon VA64 (VA64) were prepared as amorphous solid dispersions using quench cooling and hot melt extrusion. Storage stability was evaluated under a range of conditions using differential scanning calorimetry and powder X-ray diffraction. Key findings: The rank order of itz miscibility with polymers using both conventional and novel δ-based approaches was 17PF > VA64 > Soluplus > EPO, and the application of the Flory–Huggins lattice model to itz–excipient binary systems corroborated the findings. The solid-state characterisation analyses of the formulations manufactured by melt extrusion correlated well with pre-formulation screening. Long-term storage studies showed that the physical stability of 17PF/vitamin E TPGS–itz was poor compared with Soluplus and VA64 formulations, and for EPO/itz systems variation in stability may be observed depending on the preparation method. Conclusion: Results have demonstrated that although δ-based screening may be useful in predicting the initial state of amorphous solid dispersions, assessment of the physical behaviour of the formulations at relevant temperatures may be more appropriate for the successful development of commercially acceptable amorphous drug products.

Quantum control of photodissociation using intense, femtosecond pulses shaped with third order dispersion

We demonstrate the ability to control the molecular dissociation rate using femtosecond pulses shaped with third-order dispersion (TOD). Explicitly, a significant 50% enhancement in the dissociation yield for the low lying vibrational levels (v ∼ 6) of an H+2 ion-beam target was measured as a function of TOD. The underlying mechanism responsible for this enhanced dissociation was theoretically identified as non-adiabatic alignment induced by the pre-pulses situated on the leading edge of pulses shaped with negative TOD. This control scheme is expected to work in other molecules as it does not rely on specific characteristics of our test-case H+2 molecule.