Ground-based mechanized forestry requires the traversal of terrain by heavy machines. The routes they take are often called machine trails, and are created by removing trees from the trail and placing the logs outside it. Designing an optimal machine trail network is a complex locational problem that requires understanding how forestry machines can operate on the terrain as well as the trade-offs between various economic and ecological aspects. Machine trail designs are currently created manually based on intuitive decisions about the importance, correlations, and effects of many potentially conflicting aspects. Badly designed machine trail networks could result in costly operations and adverse environmental impacts. Therefore, this study was conducted to develop a holistic optimization framework for machine trail network design. Key economic and ecological objectives involved in designing machine trail networks for mechanized cut-to-length operations are presented, along with strategies for simultaneously addressing multiple objectives while accounting for the physical capabilities of forestry machines, the impact of slope, and operating costs. Ways of quantitatively formulating and combining these different aspects are demonstrated, together with examples showing how the optimal network design changes in response to various inputs.

This paper proposes novel low-profile differential-fed planar antennas with embedded sharp frequency selectively. The antennas are compact and easy to integrate with differential devices without matching baluns. The antenna design is formulated as a topology optimization problem, where requirements on impedance bandwidth, directivity, and filtering are used as the design objectives. The optimized antennas operate over the frequency band 6.0-8.5 GHz. The antennas have reflection coefficients below -15 dB, cross-polarization levels below -42 dB, a maximum gain of 6.0 +/- 0.5 dB, and a uniform directivity over more than 130 degrees beamwidth angle in the frequency band of interest. In addition, the antennas exhibit sharp roll-off between the operational band and frequencies around the 5.8 GHz WiFi band and the 10 GHz X-band. One antenna has been fabricated with a good match between simulation and measurement results.

The problem of moving a commodity with a given initial mass distribution to a pre-specified target mass distribution so that the total work is minimized can be traced back at least to Monge’s work from 1781. Here, we consider a version of this problem aiming to minimize a combination of road construction and transportation cost by determining, at each point, the local direction of transportation. This paper covers the modeling of the problem, highlights how it can be formulated as a material distribution topology optimization problem, and shows some results.

This paper considers multiscale topology optimization of an infill structure. The structure is composed of an optimized layout configuration at the macroscale with uniform optimized microstructures as infill lattices coated by a thin skin. The design optimization of the infill lattice is performed simultaneously with the topology optimization of macroscale structure, which also includes the coating. The classic Solid Isotropic Material with Penalization (SIMP) method is used to design the topology of the microstructure. The Novel Implementation of Asymptotic Homogenization (NIAH) evaluates the effective properties of the microstructure; the macroscale structural analysis uses these homogenized properties. The Porous Anisotropic Material with Penalization (PAMP) method realizes the two-scale topology optimization of the macrostructure and microstructure. Harmonic-mean based non-linear filters identify the coating layer and realize length scale control on the topologies of the macrostructure as well as the microstructure. These morphology mimicking non-linear filters also distinguish the skin and the infill lattice parts of the design at the macroscale. Several numerical examples demonstrate the effectiveness of the proposed optimization method.

This paper deals with equal-width length-scale control in topology optimization. To realize this aim, we first review different notions of minimum and maximum length-scale control and highlight some perhaps counterintuitive consequences of the various definitions. Here, we implement equal-width control within the moving morphable components (MMC) framework by imposing the same upper and lower bounds on the width of the components. To avoid partially overlapping beams and nearly parallel beams, as well as beams crossing at small angles, we introduce penalty functions of the angle difference and the minimum distance between any two beams. A penalized optimization formulation of compliance minimization is established and studied in several numerical examples with different load cases and boundary conditions. The numerical results show that equal-width length-scale control can be obtained by using the proposed penalty function in combination with a continuation approach for the amount of penalization.

Constantly growing power consumption of data centers is a major concern from environmental and economical reasons. Current approaches to reduce negative consequences of high power consumption focus on limiting the peak power consumption. During high workload periods, power consumption of highly utilized servers is throttled to stay within the power budget. However, the peak power reduction affects performance of hosted applications and thus leads to Quality of Service violations. In this paper, we introduce Power Shepherd, a hierarchical system for application performance aware power shifting.

Power Shepherd reduces the data center operational costs by redistributing the available power among applications hosted in the cluster. This is achieved by, assigning server power budgets by the cluster controller, enforcing these power budgets using Running Average Power Limit (RAPL), and prioritizing applications within each server by adjusting the CPU scheduling configuration. We implement a prototype of the proposed solution and evaluate it in a real testbed equipped with power meters and using representative cloud applications. Our experiments show that Power Shepherd has potential to manage a cluster consisting of thousands of servers and limit the increase of operational costs by a significant amount when the cluster power budget is limited and the system is overutilized. Finally, we identify some outstanding challenges regarding model sensitivity and the fact that this approach in its current from is not beneficial to be usedin all situations, e.g., when the system is underutilized

A compression driver is an electro-acoustic transducer with considerably higher efficiency than direct radiating loudspeakers, thanks to the increased radiation resistance caused by a large vibrating diaphragm placed in a compression chamber with small openings. The transition section between compression chamber and output waveguide, the phase plug, must be carefully designed to avoid irregularities in the output sound pressure level (SPL) as a function of frequency. Here we present a shape optimization method based on an implicit level-set description and adjoint sensitivity analysis, which enables a large number of design parameters and vast design freedom. The CutFEM approach, a fictitious domain finite element method, removes the need for mesh updates and makes the method robust and computationally inexpensive. Numerical experiments for a generic annular diaphragm compression driver are presented, with optimized designs showing only minor frequency irregularities. Two different objective functions are considered: one for maximum SPL and one where the SPL is fitted to that of a hypothetical ideal design; the latter approach is found to be more effective in reducing irregularities. Visco-thermal boundary-layer losses are included in a post-processing step, and, though the influence of losses is clearly noticeable, the overall performance is similar and the optimized designs still outperform the original design.

Fictitious domain methods are attractive for shape optimization applications, since they do not require deformed or regenerated meshes. A recently developed such method is the CutFEM approach, which allows crisp boundary representations and for which uniformly well-conditioned system matrices can be guaranteed. Here, we investigate the use of the CutFEM approach for acoustic shape optimization, using as test problem the design of an acoustic horn for favorable impedance-matching properties. The CutFEM approach is used to solve the Helmholtz equation, and the geometry of the horn is implicitly described by a level-set function. To promote smooth algorithmic updates of the geometry, we propose to use the nodal values of the Laplacian of the level-set function as design variables. This strategy also improves the algorithm's convergence rate, counteracts mesh dependence, and, in combination with Tikhonov regularization, controls small details in the optimized designs. An advantage with the proposed method is that the exact derivatives of the discrete objective function can be expressed as boundary integrals, as opposed to when using a traditional method that uses mesh deformations. The resulting horns possess excellent impedance-matching properties and exhibit surprising subwavelength structures, not previously seen, which are possible to capture due to the fixed mesh approach.

Server power capping limits the power consumption of a server to not exceed a specific power budget. This allows data center operators to reduce the peak power consumption at the cost of performance degradation of hosted applications. Previous work on server power capping rarely considers Quality-of-Service (QoS) requirements of consolidated services when enforcing the power budget. In this paper, we introduce ALPACA, a framework to reduce QoS violations and overall application performance degradation for consolidated services. ALPACA reduces unnecessary high power consumption when there is no performance gain, and divides the power among the running services in a way that reduces the overall QoS degradation when the power is scarce. We evaluate ALPACA using four applications: MediaWiki, SysBench, Sock Shop, and CloudSuite’s Web Search benchmark. Our experiments show that ALPACA reduces the operational costs of QoS penalties and electricity by up to 40% compared to a non optimized system. 

In this paper, we investigate the use of fat tissue as a communication channel between in-body, implanted devices at R-band frequencies (1.7–2.6 GHz). The proposed fat channel is based on an anatomical model of the human body. We propose a novel probe that is optimized to efficiently radiate the R-band frequencies into the fat tissue. We use our probe to evaluate the path loss of the fat channel by studying the channel transmission coefficient over the R-band frequencies. We conduct extensive simulation studies and validate our results by experimentation on phantom and ex-vivo porcine tissue, with good agreement between simulations and experiments. We demonstrate a performance comparison between the fat channel and similar waveguide structures. Our characterization of the fat channel reveals propagation path loss of ∼0.7 dB and ∼1.9 dB per cm for phantom and ex-vivo porcine tissue, respectively. These results demonstrate that fat tissue can be used as a communication channel for high data rate intra-body networks.