Dynamic Program Analysis Research Articles

Algorithmic formulation of clay and sand pipe–soil interaction models for on-bottom stability analysis

This paper presents a new algorithmic formulation of the clay and sand pipe–soil interaction models recommended by the DNV-RP-F109 code for dynamic on-bottom stability analysis of submarine pipelines. The pipe–soil force update algorithm is formulated within the framework of computational elasto-plasticity and applies Backward-Euler integration to ensure stability and robustness for large time step sizes. Algorithmic optimization techniques are developed by utilizing a closed-form solution and subincrementation. A numerical verification study covering full cyclic displacement ranges of a 12 inch pipeline is presented. The new formulation is shown to increase the time step size by a factor of up to 50 compared to commercial software tools for on-bottom stability analysis. This achievement will be particularly beneficial for long-duration 3D nonlinear time domain on-bottom stability analysis.

Plasticity-on-Chip Design: Exploiting Self-Similarity for Data Communications

With the increasing demand for distributed big data analytics and data-intensive programs which contribute to large volumes of packets among processing elements (PEs) and memory banks, we witness a pressing need for new mathematical models and algorithms that can engineer a brain-inspired plasticity into the computing platforms by mining the topological complexity of high-level programs (HLPs) and exploiting their self-similar and fractal characteristics for designing reconfigurable domain-specific computing architectures. In this article, we present Plasticity-on-Chip (PoC) by engineering plasticity into ”artificial brains” to mine and exploit the self-similarity of HLPs. First, we present a communication modeling of HLPs (e.g., C/C++ implementations of various applications) that relies on static and dynamic compiler analysis of programs with varying input seeds, performing comprehensive program analysis of all traces, and representing the HLPs as weighted directed acyclic graphs while capturing the intrinsic timing constraints and data/control flow requirements. Second, we propose a rigorous mathematical framework for determining the optimal parallel degree of executing a set of interacting HLPs (by partitioning them into clusters of densely interconnected supernodes - tasks) which helps us decide the number of available heterogeneous PEs, the amount of required memory and the structure of the synthesized deadlock-free irregular NoC topology that offers an efficient communication medium. These clusters serve as abstract models of computation for the synthesized PEs within the parallel execution model. Finally, exploiting the fractal and complex networks concepts, we extract in-depth features from graphs that serve as inputs for distributed reinforcement learning. Our experimental results on synthesized PEs and NoCs show performance improvements as high as 7.61x when compared to the traditional NoC and 2.6x compared to gem5-Aladdin.

Analysis and Application of Dynamic Programming

Dynamic programming is a part that is not well understood in the introductory learning of algorithms, but it is also a part worth learning. It has been successfully applied in many fields, such as gene sequencing, human flow control, and hydropower resource allocation. This article systematically explains the principle of dynamic programming. At the same time, through the comparison with other algorithms, we can deeply understand the nature of dynamic programming and its advantages and disadvantages in solving problems compared with other algorithms. Finally, it analyzes the problem-solving methods and steps of dynamic programming based on relevant application examples.

Seismic Energy Response of SDOF Systems Subjected to Long-Period Ground Motion Records

To provide an important reference for the energy-based seismic design of long-period structures, the elastoplastic dynamic analysis program is employed to study the seismic energy response of single-degree-of-freedom (SDOF) systems under two types of typical long-period ground motions. Then, the influencing relationships of external and internal factors on the energy response spectra under near-fault pulse-like and far-field harmonic ground motions are analyzed one by one. Study results are obtained as follows: within the whole period, all the input energy, hysteretic energy and damping energy spectra of SDOF systems under near-fault pulse-like and far-field harmonic ground motions, are larger than those under common ground motions, even the seismic energy response under far-field harmonic ground motions is larger than that under near-fault pulse-like ground motions. From the aspect of energy concept, the energy response spectra and energy distribution rule of SDOF systems are evaluated based on the intensity and spectral distribution under near-fault pulse-like and far-field harmonic ground motions. If the ratio of hysteretic energy to input energy (RHEIE) is determined, the hysteretic energy which must be dissipated by a structure would be derived by the method of energy-based design. The input energy and hysteretic energy are mainly influenced by damping ratio and ductility coefficient, while the yield stiffness ratio exerts minor effects. It indicates that reasonable structural design parameters would contribute to the hysteretic energy of a structure itself.

PortAuthority: Integrating energy efficiency analysis into cross-platform development cycles via dynamic program analysis

Source code improvements can be suggested or automatically applied from within an IDE augmenting the skills of a developer. In addition, compiler optimizations are readily available for a variety of languages and programming environments that continue to enhance a developer's code on the back end. Common optimizations include transformations that automatically improve the speed or lower the memory usage of an application. However, no equivalent options exist to tailor the compilation, linking or refactoring of a program for better energy efficiency. This paper fills the gap by proposing a methodology that can integrate energy efficiency analysis into software development cycles via dynamic program analysis. Our goal is to provide the same usability and increased code quality that are available for performance-oriented optimizations in the context of a development environment but for issues concerning power consumption. We explore using PortAuthority as an easy-to-use tool that can increase developer awareness of energy related improvements via both descriptive messages and indicators of severity. By analyzing the power efficiency of basic instructions at the binary level and building generalized rules for improving code's energy efficiency, our approach is language agnostic, cross-platform, and can be seamlessly integrated to popular IDEs. We present the integrated solution to the Visual Studio Code IDE and demonstrate its usage in several sample projects.

Dynamic Programming Analysis for Measuring the Optimization of the Manpower per Shift PT. XYZ

The problems that occur in the company is the production system make to order, for example PT. XYZ. Unknown certainty the amount of the order lenses will be produced. So it is not known how labor should be allocated to each shift. Then used the approach of dynamic programming to optimize the number of workers per shift in the PT. XYZ. This study aims to determine the analysis of the dynamic programming optimization to allocate the amount of labor per shift to be effective. The type of research data is primary data obtained through direct observation in PT. XYZ. The results of the research based on the calculation of the forward recursive dynamic programming: shift 1 = 105 people, shift 2 = 104 people and shift 3 = 104 people. While the solution of the optimal allocation of labor for each shift based on the calculation of the backward recursive dynamic programming: shift 1 = 105 people, shift 2 = 104 people and Shift 3 = 104 people. Then the PT. XYZ should allocate 313 workers, so that the allocation of labor to each shift is optimal according to the order received

Flow Angularity and Swirl Flow Analysis on Transonic Compressor Rotor by 1-Dimensional Dynamic Turbine Engine Compressor Code and CFD Analysis

The performance and stability of a low-bypass turbofan transonic axial compressor with a nonuniform inlet flow is a significant concern in recent times. In both military and commercial aircraft, serpentine ducts produce significant inlet swirl distortion. Moreover, the nonuniform inlet flow frequently acts upon aircraft gas-turbine engines causing deteriorating effects on the aircraft engine. High circumferential swirl flows and inlet flow angularity decrease the aerodynamic performance and the stall margin and increase the rotor blade loading. The current paper is aimed at the investigation of the flow field in the tip clearance region of low-bypass turbofan transonic compressor rotor under nonuniform circumferential flow conditions through numerical simulation using Ansys CFX. The mathematical models based on 1D Mean Line Code and Dynamic Turbine Engine Compressor Code (DYNTECC) are used to analyze the nonuniform inlet swirl flow of the compressor rotor. The mathematical model is limited to compute the multistage compressor characteristics for the compression system and the combustor of a turbine engine. For the single-stage swirl flow analysis current paper focuses on the CFD based results. The results based on CFD show that co-swirl patterns slightly improve the stability range of the compressor; counter-swirl flows diminish it.

Extension of ALE method in large deformation analysis of saturated soil under earthquake loading

This article develops an Arbitrary Lagrangian Eulerian (ALE) finite element program for liquefaction dynamic analysis of saturated sand based on the updated Lagrange finite element program in U-P form. Based on the operator splitting technique, this method adopts the displacements of boundary points and Radial Basis Function (RBF) method to optimize the mesh, and employs super-convergence elements patch and Radial Basis Function (RBF) method to reconstruct variable fields and project variables between old and new configurations. The analytical solutions and numerical solutions of one-dimension consolidation are compared to verify the accuracy of the ALE method. Three typical dynamic liquefaction problems of saturated sand are implemented to measure the applicability of UL method and ALE method. The results show that both methods can provide similar results in the case of small earthquakes. Under strong earthquakes, the UL method fails for large-scale or local mesh distortion, while the proposed ALE can still maintain the health of the mesh and provide satisfactory results.

SAPAVE: an improved semi-analytical FE program for dynamic viscoelastic analysis of asphalt pavement

ABSTRACT Analyzing dynamic viscoelastic responses induced by moving loads is a significant issue for pavement design and performance evaluation. Developing three-dimensional FE models in commercial software is an effective method, but the time-consuming disadvantage prevents it from extensive engineering applications. In this paper, an improved semi-analytical FE model was proposed to overcome this drawback. Besides, modified artificial boundaries were applied to minimise the interference of reflected waves generated by model edges. Further, a program named SAPAVE has been developed in MATLAB to achieve the modeling procedure, of which accuracy and efficiency were verified by comparing with the three-dimensional FE model in ABAQUS. The computing cost of SAPAVE was only one-sixth of ABAQUS, while SAPAVE can roughly achieve the function of ABAQUS for simple pavement modeling. The response considering the asphalt layer as viscoelastic was quite different from the linear elastic in the comparative study. The viscoelastic analysis seems to be closer to the field situation. Besides, the impact of vehicle speed was studied to examine the application ability of SAPAVE, and the results were generally consistent with existing researches. Overall, this study indicates that SAPAVE is an efficient and accurate tool for mechanical analysis of asphalt pavement.

ОБНАРУЖЕНИЕ СЕРВЕРНЫХ ТОЧЕК ВЗАИМОДЕЙСТВИЯ В ВЕБ-ПРИЛОЖЕНИЯХ НА ОСНОВЕ АНАЛИЗА

The problem of server-side endpoint detection in the context of blackbox security analysis of dynamic web applications is considered. We propose a method to increase coverage of server-side endpoint detection using static analysis of client-side JavaScript code to find functions which generate HTTP requests to the server-side of the application and reconstruct parameters for those functions. In the context of application security testing, static analysis allows to find such functions even in dead or unreachable JavaScript code, which cannot be achieved by dynamic crawling or dynamic code analysis. Evaluation of the proposed method and its implementation has been done using synthetic web application with endpoints vulnerable to SQL injections, and the same application was used to compare the proposed method with existing solutions. Evaluation results show that adding JavaScript static analysis to traditional dynamic crawling of web applications may significantly improve server-side endpoint coverage in blackbox application security analysis.

An efficient and direct method for trajectory optimization of robots constrained by contact kinematics and forces

In this work, we propose a trajectory generation method for robotic systems with contact kinematics and force constraints based on optimal control and reachability analysis tools. Normally, the dynamics and constraints of a contact-constrained robot are nonlinear and coupled to each other. Instead of linearizing the model and constraints, we solve the optimal control problem directly to obtain feasible state trajectories and their corresponding control inputs. A tractable optimal control problem is formulated and subsequently addressed by dual approaches, which rely on sampling-based dynamic programming and rigorous reachability analysis tools. In particular, a sampling-based method together with a Partially Observable Markov Decision Process solution approach are used to break down the end-to-end trajectory generation problem by generating a sequence of subregions that the system’s trajectory will have to pass through to reach its final destination. The distinctive characteristic of the proposed trajectory optimization algorithm is its ability to handle the intricate contact constraints, coupled with the system dynamics, in a computationally efficient way. We validate our method using extensive numerical simulations with two legged robots.

Benchmark Analysis of FFTF Unprotected Loss of Flow without Scram Test No.13 with Fast Reactor Plant Dynamics Analysis Code Super-COPD

Benchmark Analysis of FFTF Unprotected Loss of Flow without Scram Test No.13 with Fast Reactor Plant Dynamics Analysis Code Super-COPD

Automated Large-scale Multi-language Dynamic Program Analysis in the Wild

Automated Large-scale Multi-language Dynamic Program Analysis in the Wild

Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.

Stochastic modelling of maintenance flexibility in Value for Money assessment of PPP road projects

Maintenance flexibility has been promoted as a value driver for long-term public–private partnerships (PPPs). However, the value and risk associated with this value driver have not been properly quantified in the Value for Money (VfM) assessment literature. To bridge the gap, a novel stochastic modelling methodology is proposed to characterize the complex interactions among the lifecycle cost (LCC), performance deterioration and maintenance strategies. Four different maintenance strategies are designed to emulate the practice in the traditional and PPP delivery methods. The LCC includes the direct maintenance cost, user cost, residual value, and payment deduction, the last three often being neglected in VfM assessments. Simulation-based optimization and dynamic programming analysis are used to determine the probability distributions of the LCC and the VfM. A hypothetical highway PPP project under an availability payment model is selected as a case study. The results show that maintenance flexibility is indeed able to reduce the LCC for the private party. However, this private efficiency, if not properly regulated, could cause a reduced asset residual value and an increased user cost, making the public party worse off. In addition, for all potential maintenance strategies, the public sector is found to retain significant lifecycle cost risk, largely in the form of user cost.

Assessing hydropower flexibility for integrating solar and wind energy in West Africa using dynamic programming and sensitivity analysis. Illustration with the Akosombo reservoir, Ghana

The flexibility of hydropower plants with large reservoirs is frequently exploited to integrate large shares of variable and intermittent renewable energy sources in electricity systems. In this study, we assess the flexibility that could be provided by large hydropower reservoirs in West Africa to cope with planned future solar and wind energy generation in the region. Reservoir operations are estimated via Dynamic Programming with the objective to minimize the variability of the residual demand that commonly needs to be supplied by conventional generation means at high monetary and carbon costs. The analysis framework is demonstrated for the Akosombo hydropower reservoir in Ghana for which a number of future scenarios of increased electricity demand are considered. Different combinations of solar and wind energy development are considered to match the increase in demand. The results show that the Akosombo hydropower reservoir can smooth out the variability of the residual electricity demand when the increase in electricity demand is below 25% and the corresponding contribution of solar and wind energy to the total electricity generation does not exceed 20%. For larger increases in demand and thus larger solar and wind generation, the Akosombo reservoir cannot fully smooth-out the variability of the subsequent residual demand, although, the performance varies with the relative contribution of solar and wind in the energy mix. However, we found that the use of an additional short-term storage helps to further reduce the variability of the residual demand.

Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning

Abstract Fueled by advertising companies’ need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users’ privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code and machine learning for the automatic detection of web fingerprinters. We apply our methodology on a dataset of more than 400, 000 JavaScript files accessed by about 1, 000 volunteers during a one-month long experiment to observe adoption of fingerprinting in a real scenario. We compare approaches based on both static and dynamic code analysis to automatically detect fingerprinters and show they provide different angles complementing each other. This demonstrates that studies based on either static or dynamic code analysis provide partial view on actual fingerprinting usage in the web. To the best of our knowledge we are the first to perform this comparison with respect to fingerprinting. Our approach achieves 94% accuracy in small decision time. With this we spot more than 840 fingerprinting services, of which 695 are unknown to popular tracker blockers. These include new actual trackers as well as services which use fingerprinting for purposes other than tracking, such as anti-fraud and bot recognition.

Modeling and Analysis of Suspension Frame of Maglev Train

Based on the acceleration feedback system, the vertical dynamic model of the low-speed maglev train is established. By decoupling the suspension frame module, the author conducted a stress analysis of the single-sided and the entire suspension system, and established a system dynamics model of the suspension frame. The suspension controller is designed using classic PD control and acceleration feedback control methods. Then, the author used Simulink to create a dynamic simulation analysis program to analyze the dynamic performance of the suspension system affected by track misalignment and external disturbance forces, and analyze the impact of the suspension frame’s parameter design on the suspension system performance.

Research and Improvement of Calculation Method of Flow Pressure in Mechanical Recovery Wells

This paper takes the flow pressure of oil recovery wells in polymer flooding blocks in the past two years as the research data, and conducts field test research based on the analysis of test principles and technical indicators of the equipment. By taking the oil wells at the actual static pressure measurement points as the research object, the author compares the flow pressure of the static fluid with that calculated by the low-pressure test data management and analysis software so as to analyze the rationality and data modification method of the formula. By giving full play to the role of the data of the flow pressure and working liquid level in the dynamic analysis and development program preparation, this paper formulates a reasonable working system for mechanical recovery wells, thereby helping to ensure the sustainable and stable production of oil fields.The low-pressure test team is responsible for the test of the indicator diagram and working liquid level of the oil well in the polymer flooding block. Comprehensive data such as the indicator diagram, working liquid level, depth of the middle layer, pump depth, depth of pump inlet, and casing pressure, as well as comprehensive water cut are imported into the data management and analysis software, which are used to calculate the flow pressure of the oil wells.

A Compositional Framework for Scientific Model Augmentation

Scientists construct and analyze computational models to understand the world. That understanding comes from efforts to augment, combine, and compare models of related phenomena. We propose SemanticModels.jl, a system that leverages techniques from static and dynamic program analysis to process executable versions of scientific models to perform such metamodeling tasks. By framing these metamodeling tasks as metaprogramming problems, SemanticModels.jl enables writing programs that generate and expand models. To this end, we present a category theory-based framework for defining metamodeling tasks, and extracting semantic information from model implementations, and show how this framework can be used to enhance scientific workflows in a working case study.