The influence of self‐weight of elastic 2D structures in topology optimization via numerical technique Smooth Evolutionary Structural Optimization (SESO)

O presente artigo aborda a otimização topológica em problemas de elasticidade plana linear considerando a influência do peso próprio nos esforços em elementos estruturais. Utiliza‐se para este fim uma técnica numérica denominada Smooth ESO (SESO) que se baseia no procedimento de diminuição progressiva da contribuição de rigidez de elementos ineficientes com menores tensões até que ele não tenha mais influência. As aplicações do SESO são feitas com o método dos elementos finitos e considera‐se um elemento finito triangular e de alta ordem. Neste trabalho estende‐se a técnica SESO para a aplicação do peso próprio onde o programa, no cômputo de seu volume e peso específico, gera automaticamente uma força concentrada equivalente para cada nó do elemento. A avaliação é finalizada com a definição de um modelo de bielas e tirantes resultante das regiões de concentração de tensões. Nos exemplos de aplicação são apresentadas topologias ótimas de uma estrutura suspensa, de viga baixa e de viga parede considerando o peso próprio e obtendo‐se ótimas configurações e demonstrando que a consideração do peso próprio leva a maior robustez ao processo de otimização.This paper deals with topology optimization in plane elastic‐linear problems considering the influence of the self weight in efforts in structural elements. For this purpose it is used a numerical technique called SESO (Smooth ESO), which is based on the procedure for progressive decrease of the inefficient stiffness element contribution at lower stresses until he has no more influence. The SESO is applied with the finite element method and is utilized a triangular finite element and high order. This paper extends the technique SESO for application its self weight where the program, in computing the volume and specific weight, automatically generates a concentrated equivalent force to each node of the element. The evaluation is finalized with the definition of a model of strut‐and‐tie resulting in regions of stress concentration. Examples are presented with optimum topology structures obtaining optimal settings.Peer Reviewe

Optimal topology seletion for 2D structures with stress constraints via Smooth Evolutionary Structural Optimization

O artigo aborda a otimização topológica em problemas de elasticidade plana linear considerando a minimização do volume com restrição de tensão e empregando um índice de desempenho como monitoramento para o encontro da região de ótimo. Utiliza‐se para este fim o método clássico da otimização evolucionária estrutural, ou Evolutionary Structural Optimization (ESO). Este procedimento de otimização baseia‐se na retirada sistemática e gradativa dos elementos com menores tensões em comparação com a tensão máxima da estrutura. Procedimento este também conhecido como um processo «hard‐kill». Propõe‐se neste trabalho uma variante do método ESO, denominado de Smoothing ESO (SESO), cuja filosofia baseou‐se na observação de que se o elemento não for realmente necessário à estrutura, naturalmente sua contribuição de rigidez vai diminuindo progressivamente, até que ele não tenha mais influência. Isto é, sua remoção é feita de forma suave, atenuando os valores da matriz constitutiva do elemento, como se este estivesse em processo de danificação. Define‐se também o índice de desempenho para o monitoramento deste processo evolucionário suavizado. As aplicações do ESO e do SESO são feitas com o método dos elementos finitos, mas considerando um elemento finito triangular e de alta ordem. Por fim, implementou‐se um filtro espacial em termos de controle de tensão, o qual associado à técnica SESO se mostrou ser bastante estável e eficiente na eliminação da formação do tabuleiro.This paper approaches the topology optimization problems in plane linear elasticity considering the minimization of the volume with restriction of the stress employing an index of performance for monitoring the meeting of the optimum region. It is used for this purpose the classical evolutionary structural optimization, or ESO ‐ evolutionary structural optimization. This procedure is based on systematic and gradual removal of the elements with lower stress compared with the maximum stress of the structure. This procedure also known as a process “hard‐kill”. It is proposed a variant of the ESO method, called SESO ‐ Smoothing ESO, which is based on the philosophy that if an element is not really necessary for the structure, its contribution to the structural stiffness will gradually diminish until it has no longer influence in the structure, so its removal is performed smoothly. That is, their removal is done smoothly, reducing the values of the constitutive matrix of the element as if it were in the process of damage. A new performance index for the monitoring of this evolutionary process smoothed is proposed herein. The applications of ESO and SESO are made with the finite element method, but considering a high order triangular element based on the free formulation. Finally, it is implemented a spatial filter in terms of stress control, which was associated with SESO technique proved to be very stable and efficient in eliminating the formation of the checkerboard.Peer Reviewe

Long-range Angular Correlations On The Near And Away Side In P-pb Collisions At √snn=5.02 Tev

7191/Mar294

The influence of self‐weight of elastic 2D structures in topology optimization via numerical technique Smooth Evolutionary Structural Optimization (SESO)

This paper deals with topology optimization in plane elastic‐linear problems considering the influence of the self weight in efforts in structural elements. For this purpose it is used a numerical technique called SESO (Smooth ESO), which is based on the procedure for progressive decrease of the inefficient stiffness element contribution at lower stresses until he has no more influence. The SESO is applied with the finite element method and is utilized a triangular finite element and high order. This paper extends the technique SESO for application its self weight where the program, in computing the volume and specific weight, automatically generates a concentrated equivalent force to each node of the element. The evaluation is finalized with the definition of a model of strut‐and‐tie resulting in regions of stress concentration. Examples are presented with optimum topology structures obtaining optimal settings

Health sector spending and spending on HIV/AIDS, tuberculosis, and malaria, and development assistance for health: progress towards Sustainable Development Goal 3

Background: Sustainable Development Goal (SDG) 3 aims to “ensure healthy lives and promote well-being for all at all ages”. While a substantial effort has been made to quantify progress towards SDG3, less research has focused on tracking spending towards this goal. We used spending estimates to measure progress in financing the priority areas of SDG3, examine the association between outcomes and financing, and identify where resource gains are most needed to achieve the SDG3 indicators for which data are available. Methods: We estimated domestic health spending, disaggregated by source (government, out-of-pocket, and prepaid private) from 1995 to 2017 for 195 countries and territories. For disease-specific health spending, we estimated spending for HIV/AIDS and tuberculosis for 135 low-income and middle-income countries, and malaria in 106 malaria-endemic countries, from 2000 to 2017. We also estimated development assistance for health (DAH) from 1990 to 2019, by source, disbursing development agency, recipient, and health focus area, including DAH for pandemic preparedness. Finally, we estimated future health spending for 195 countries and territories from 2018 until 2030. We report all spending estimates in inflation-adjusted 2019 US$, unless otherwise stated. Findings: Since the development and implementation of the SDGs in 2015, global health spending has increased, reaching$7·9 trillion (95% uncertainty interval 7·8–8·0) in 2017 and is expected to increase to $11·0 trillion (10·7–11·2) by 2030. In 2017, in low-income and middle-income countries spending on HIV/AIDS was$20·2 billion (17·0–25·0) and on tuberculosis it was $10·9 billion (10·3–11·8), and in malaria-endemic countries spending on malaria was$5·1 billion (4·9–5·4). Development assistance for health was $40·6 billion in 2019 and HIV/AIDS has been the health focus area to receive the highest contribution since 2004. In 2019,$374 million of DAH was provided for pandemic preparedness, less than 1% of DAH. Although spending has increased across HIV/AIDS, tuberculosis, and malaria since 2015, spending has not increased in all countries, and outcomes in terms of prevalence, incidence, and per-capita spending have been mixed. The proportion of health spending from pooled sources is expected to increase from 81·6% (81·6–81·7) in 2015 to 83·1% (82·8–83·3) in 2030. Interpretation: Health spending on SDG3 priority areas has increased, but not in all countries, and progress towards meeting the SDG3 targets has been mixed and has varied by country and by target. The evidence on the scale-up of spending and improvements in health outcomes suggest a nuanced relationship, such that increases in spending do not always results in improvements in outcomes. Although countries will probably need more resources to achieve SDG3, other constraints in the broader health system such as inefficient allocation of resources across interventions and populations, weak governance systems, human resource shortages, and drug shortages, will also need to be addressed

Underlying Event Measurements In Pp Collisions At √s = 0:9 And 7 Tev With The Alice Experiment At The Lhc

2012

Transverse momentum spectra of charged particles in proton-proton collisions at 1as=900 GeV with ALICE at the LHC

The inclusive charged particle transverse momentum distribution is measured in proton-proton collisions at s=900 GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region (|\u3b7|<0.8) over the transverse momentum range 0.15<10 GeV/c. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for |\u3b7|<0.8 is \u3008pT\u3009INEL=0.483\ub10.001 (stat.)\ub10.007 (syst.) GeV/c and \u3008pT\u3009NSD=0.489\ub10.001 (stat.)\ub10.007 (syst.) GeV/c, respectively. The data exhibit a slightly larger \u3008pT\u3009 than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET. \ua9 2010