Inertial clustering of particles in high-Reynolds-number turbulence

We report experimental evidence of spatial clustering of dense particles in homogenous, isotropic turbulence at high Reynolds numbers. The dissipation-scale clustering becomes stronger as the Stokes number increases and is found to exhibit similarity with respect to the droplet Stokes number over a range of experimental conditions (particle diameter and turbulent energy dissipation rate). These findings are in qualitative agreement with recent theoretical and computational studies of inertial particle clustering in turbulence. Because of the large Reynolds numbers a broad scaling range of particle clustering due to turbulent mixing is present, and the inertial clustering can clearly be distinguished from that due to mixing of fluid particles.     

Maximizing throughput in finite-source parallel queue systems

Motivated by the dispatching of trucks to shovels in surface mines, we study optimal routing in a Markovian finite-source, multi-server queueing system with heterogeneous servers, each with a separate queue. We formulate the problem of routing customers to servers to maximize the system throughput as a Markov Decision Process. When the servers are homogeneous, we demonstrate that the Shortest Queue policy is optimal, and when the servers are heterogeneous, we partially characterize the optimal policy and present a near-optimal and simple-to-implement policy. We use the model to illustrate the substantial benefits of pooling, by comparing it to the permanent assignment of customers to servers.     

Transport risk models for hazardous materials: revisited

Several proposed path evaluation functions for hazardous materials transport use an approximation. Modified functions that avoid the approximation violate two reasonable axioms. We present new models by redefining the decision problem as one of satisfying demand at the destination. The new models satisfy the axioms and are relatively tractable.     

A linear model for surface mining haul truck allocation incorporating shovel idle probabilities

We present models of trucks and shovels in oil sand surface mines. The models are formulated to minimize the number of trucks for a given set of shovels, subject to throughput and ore grade constraints. We quantify and validate the nonlinear relation between a shovel’s idle probability (which determines the shovel’s productivity) and the number of trucks assigned to the shovel via a simple approximation, based on the theory of finite source queues. We use linearization to incorporate this expression into linear integer programs. We assume in our integer programs that each shovel is assigned a single truck size but we outline how one could account for multiple truck sizes per shovel in an approximate fashion. The linearization of shovel idle probabilities allows us to formulate more accurate truck allocation models that are easily solvable for realistic-sized problems.     

Combining integer programming and the randomization method to schedule employees

We describe a method to find low cost shift schedules with a time-varying service level that is always above a specified minimum. Most previous approaches used a two-step procedure: (1) determine staffing requirements and (2) find a minimum cost schedule that provides the required staffing in every period. Approximations in the first step sometimes cause the two-step approach to find infeasible or suboptimal solutions. Our method iterates between a schedule evaluator and a schedule generator. The schedule evaluator calculates transient service levels using the randomization method and identifies infeasible intervals, where the service level is lower than desired. The schedule generator solves a series of integer programs to produce improved schedules, by adding constraints for every infeasible interval, in an attempt to eliminate infeasibility without eliminating the optimal solution. We present computational results for several test problems and discuss factors that make our approach more likely to outperform previous approaches.     

Methodology for measuring conformation of solvent-disrupted protein subunits using T-WAVE ion mobility MS: An investigation into eukaryotic initiation factors

The methodology developed in the research presented herein makes use of chaotropic solvents to gently dissociate subunits from an intact macromolecular complex and subsequently allows for the measurement of collision cross section (CCS) for both the recombinant (R-eIF3k) and solvent dissociated form of the subunit (S-eIF3k). In this particular case, the k subunit from the eukaryotic initiation factor 3 (eIF3) was investigated in detail. Experimental and theoretical CCS values show both the recombinant and solvent disrupted forms of the protein to be essentially the same. The ultimate goal of the project is to structurally characterize all the binding partners of eIF3, determine which subunits interact directly, and investigate how subunits may change conformation when they form complexes with other proteins. Research presented herein is the first report showing retention of solution conformation of a protein as evidenced by CCS measurements of both recombinant and solvent disrupted versions of the same protein.