Risk Assessment Model of Technogenic Pollution of the Environment from Oil Spill in the Northern Caspian Sea

Abstract

This article describes the development of a multifunctional geoinformation system RANDOM (Risk Assessment of Nature Detriment due to Oil spill Migration), realizing a multiprocessor calculation of probabilistic risk models to assess the negative impact of the oil spill on the biota of the North Caspian. The urgency of the problems associated with the development of oil fields in a very vulnerable shallow part of the Caspian Sea, where a major accident could have disastrous consequences. We describe a risk model of biota damage in the case of accidental environmental pollution. The model rests on the formalization of the notions of the accident, environmental pollution, biomass, and biota sensitivity, which depend on each other in the case of an accident. In addition, this article describes the development process from design to implementation. The system is designed on the basis of service-oriented architecture (SOA), which allows for easy, flexible integration of services, and access them via the Internet. Through the use of SOA, the system can be expanded and upgraded. In this approach, the services may be located on physically different servers. Tests have shown the benefit of using a supercomputer; it enables us to obtain a risk assessment for an adequate time. This system is designed for professionals in the field of ecology and mathematical modelling and subsoil oil fields on the continental shelf of the seas and oceans. The RANDOM system as the final result of the decision of risk assessment tasks includes a series of calculation modules based on the methods of probability theory, computational mathematics, hydrodynamics, oil chemistry, marine biology, mathematical modelling, and geoinformatics.

Design and Evaluation of a Scalable Engine for 3D-FFT Computation in an FPGA Cluster

“…Continuous demand for efficient computing power is pushing designers into integrating dedicated hardware components in High-Performance Computing (HPC) architectures to improve computational efficiency. Generalpurpose CPUs can delegate specific tasks to the hardware accelerator decreasing the latency of computationally demanding task such as the training of neural networks [1]- [4], video or audio processing [5]- [8], environmental forecasting [9]- [11], security algorithms [12], automotive applications [13], etc. In other scenarios, hardware accelerators are used to reduce system power consumption [14]- [15].…”