Dynamic response simulation for a nonlinear system.

Laboratory simulation testing has for many years contributed significantly to the durability and quality of motor vehicles. Most sophisticated test rigs use an iterative algorithm that generates the input drive files that reproduce service environments under laboratory conditions. Essentially the algorithm solves a nonlinear, multiple channel dynamic system. In this paper, the nonlinear problem is recast as a system of algebraic equations. This mathematical framework allows the application of alternative but well understood solution techniques. Using mathematical simulations, conclusions are drawn concerning the choice of iteration gain in the current algorithm and the better performance of alternative numerical solution procedures

Dynamic response simulation through system identification.

Nonlinear dynamic systems, such as those associated with structural testing of vehicles, are considered. The vehicle, or a substructure, is mounted in a test rig that is normally driven by servo-hydraulic actuators. The specimen and test rig form a nonlinear dynamic system. These test systems assure the durability of vehicles by reproducing a structural response time history that has been measured in a road test of a vehicle. For this, a force or displacement input to the actuators’ control system must be determined as a function of time.Current practice employs an iterative algorithm, using a frequency response function to represent the system. The conventional iteration is a particular version of well established numerical techniques for solving nonlinear systems. However, the success of the iteration is dependent on the degree of nonlinearity and on the level of noise in the signals coming from the system.This paper advocates identifying the system to improve its representation in the iterative algorithm. The theory underpinning the alternative algorithm is presented and a comparison is made between the performances of the two algorithms, using computer simulations based on Duffing's equation. These simulations show that, even for this simple model, the alternative algorithm is faster, more reliable and more tolerant of response noise

Diffusion and Innovation Theory: Past, Present, and Future Contributions to Academia and Practice

Part 4: PanelInternational audienceThe field of information systems (IS) has throughout its history experienced extensive changes in technology, research, and education. These renewals will continue into the foreseeable future [10]. It is recognized that IS is a key force in the ongoing societal and organizational renewal and change [2, 8, 14]. For example, in the US business sector, IS continues yearly to consume about 30% of total investments made [5]. Recent research document that IS supports the creation of business value, with particular emphasis on an organization’s innovation and change capabilities [1, 3]. Traditionally, research in IS has been interdisciplinary in nature - since it draws on innovation theory, models of value creation, actors’ roles and behaviors, the creation and running of task oriented groups, and how these relate to organizational structures and mechanisms [24]. Throughout its history the question of benefits from investing in IS has been lively discussed

Anthropogenic Space Weather

Anthropogenic effects on the space environment started in the late 19th century and reached their peak in the 1960s when high-altitude nuclear explosions were carried out by the USA and the Soviet Union. These explosions created artificial radiation belts near Earth that resulted in major damages to several satellites. Another, unexpected impact of the high-altitude nuclear tests was the electromagnetic pulse (EMP) that can have devastating effects over a large geographic area (as large as the continental United States). Other anthropogenic impacts on the space environment include chemical release ex- periments, high-frequency wave heating of the ionosphere and the interaction of VLF waves with the radiation belts. This paper reviews the fundamental physical process behind these phenomena and discusses the observations of their impacts.Comment: 71 pages, 35 figure

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Influencing resilience: The role of policy entrepreneurs in mainstreaming climate adaptation

One way to make development pathways more resilient in the face of a changing climate has been through mainstreaming adaptation into government policies, planning and sectoral decision‐making. To date, many of the transferable lessons have taken the form of technical approaches such as risk assessments and toolkits. This article instead draws on evidence from South Asia to emphasise some of the more tacit and informal approaches used to influence adaptation policy. Despite their apparent significance in policy processes, such tactics are often neither planned for nor well reported in resilience‐building projects and programme documents. Using evidence to populate a typology of influencing strategies, this article looks particularly at the role of policy entrepreneurs who navigate the political complexity of both formal and informal governance systems to promote successful adaptation mainstreaming. It concludes with recommendations for adaptation and resilience programming that can more effectively harness the breadth of influencing strategies

On the mechanisms governing gas penetration into a tokamak plasma during a massive gas injection

A new 1D radial fluid code, IMAGINE, is used to simulate the penetration of gas into a tokamak plasma during a massive gas injection (MGI). The main result is that the gas is in general strongly braked as it reaches the plasma, due to mechanisms related to charge exchange and (to a smaller extent) recombination. As a result, only a fraction of the gas penetrates into the plasma. Also, a shock wave is created in the gas which propagates away from the plasma, braking and compressing the incoming gas. Simulation results are quantitatively consistent, at least in terms of orders of magnitude, with experimental data for a D 2 MGI into a JET Ohmic plasma. Simulations of MGI into the background plasma surrounding a runaway electron beam show that if the background electron density is too high, the gas may not penetrate, suggesting a possible explanation for the recent results of Reux et al in JET (2015 Nucl. Fusion 55 093013)

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR