Application of Multi Objective Genetic Algorithm for Optimization of Core Configuration Design of a Fast Breeder Reactor

The optimization problem of nuclear fuel management, reported in the present  study aimed at arriving at the optimal number of subassemblies in the two fuel enrichment zones of the core of a 500 MWe Fast Breeder Reactor. The elitist multi-objective approach of Genetic Algorithm, namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II), was employed in the study. The five parameters considered for optimization are: core excess reactivity, liner heat ratings of inner and outer fuel enrichment zones of the core, fissile material inventory, and breeding ratio. The results obtained from the study indicate that the algorithm is able to produce feasible solutions in an efficient manner while preserving the diversity amongst them. The fast convergence and the diversity-preserving feature of the algorithm are described. The major objective of the work is to study the viability of applying the NSGA-II into the nuclear fuel management problems of fast breeder reactors

Code Reviewer Recommendations as a Multi-Objective Problem: Balancing Expertise, Availability and Collaborations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156106/1/ASE_J_Multi_Objective_Code_Reviewer_assignment_FV__Copy_ (1).pdfSEL

Scaling Distributed Ledgers and Privacy-Preserving Applications

This thesis proposes techniques aiming to make blockchain technologies and smart contract platforms practical by improving their scalability, latency, and privacy. This thesis starts by presenting the design and implementation of Chainspace, a distributed ledger that supports user defined smart contracts and execute user-supplied transactions on their objects. The correct execution of smart contract transactions is publicly verifiable. Chainspace is scalable by sharding state; it is secure against subsets of nodes trying to compromise its integrity or availability properties through Byzantine Fault Tolerance (BFT). This thesis also introduces a family of replay attacks against sharded distributed ledgers targeting cross-shard consensus protocols; they allow an attacker, with network access only, to double-spend resources with minimal efforts. We then build Byzcuit, a new cross-shard consensus protocol that is immune to those attacks and that is tailored to run at the heart of Chainspace. Next, we propose FastPay, a high-integrity settlement system for pre-funded payments that can be used as a financial side-infrastructure for Chainspace to support low-latency retail payments. This settlement system is based on Byzantine Consistent Broadcast as its core primitive, foregoing the expenses of full atomic commit channels (consensus). The resulting system has extremely low-latency for both confirmation and payment finality. Finally, this thesis proposes Coconut, a selective disclosure credential scheme supporting distributed threshold issuance, public and private attributes, re-randomization, and multiple unlinkable selective attribute revelations. It ensures authenticity and availability even when a subset of credential issuing authorities are malicious or offline, and natively integrates with Chainspace to enable a number of scalable privacy-preserving applications

Application of Multi Objective Genetic Algorithm for Optimization of Core Configuration Design of a Fast Breeder Reactor

The optimization problem of nuclear fuel management, reported in the present study aimed at arriving at the optimal number of subassemblies in the two fuel enrichment zones of the core of a 500 MWe Fast Breeder Reactor. The elitist multi-objective approach of Genetic Algorithm, namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II), was employed in the study. The five parameters considered for optimization are: core excess reactivity, liner heat ratings of inner and outer fuel enrichment zones of the core, fissile material inventory, and breeding ratio. The results obtained from the study indicate that the algorithm is able to produce feasible solutions in an efficient manner while preserving the diversity amongst them. The fast convergence and the diversity-preserving feature of the algorithm are described. The major objective of the work is to study the viability of applying the NSGA-II into the nuclear fuel management problems of fast breeder reactors

Particle computation: Designing worlds to control robot swarms with only global signals

Micro- and nanorobots are often controlled by global input signals, such as an electromagnetic or gravitational field. These fields move each robot maximally until it hits a stationary obstacle or another stationary robot. This paper investigates 2D motion-planning complexity for large swarms of simple mobile robots (such as bacteria, sensors, or smart building material). In previous work we proved it is NP-hard to decide whether a given initial configuration can be transformed into a desired target configuration; in this paper we prove a stronger result: the problem of finding an optimal control sequence is PSPACE-complete. On the positive side, we show we can build useful systems by designing obstacles. We present a reconfigurable hardware platform and demonstrate how to form arbitrary permutations and build a compact absolute encoder. We then take the same platform and use dual-rail logic to build a universal logic gate that concurrently evaluates AND, NAND, NOR and OR operations. Using many of these gates and appropriate interconnects we can evaluate any logical expression.National Science Foundation (U.S.) (CPS-1035716

Refactorings Recommendation via Commit Message Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155331/1/Commit_Messages_Analysis_for_Refactoring__Copy_ (16).pd

Particle Computation: Complexity, Algorithms, and Logic

We investigate algorithmic control of a large swarm of mobile particles (such as robots, sensors, or building material) that move in a 2D workspace using a global input signal (such as gravity or a magnetic field). We show that a maze of obstacles to the environment can be used to create complex systems. We provide a wide range of results for a wide range of questions. These can be subdivided into external algorithmic problems, in which particle configurations serve as input for computations that are performed elsewhere, and internal logic problems, in which the particle configurations themselves are used for carrying out computations. For external algorithms, we give both negative and positive results. If we are given a set of stationary obstacles, we prove that it is NP-hard to decide whether a given initial configuration of unit-sized particles can be transformed into a desired target configuration. Moreover, we show that finding a control sequence of minimum length is PSPACE-complete. We also work on the inverse problem, providing constructive algorithms to design workspaces that efficiently implement arbitrary permutations between different configurations. For internal logic, we investigate how arbitrary computations can be implemented. We demonstrate how to encode dual-rail logic to build a universal logic gate that concurrently evaluates and, nand, nor, and or operations. Using many of these gates and appropriate interconnects, we can evaluate any logical expression. However, we establish that simulating the full range of complex interactions present in arbitrary digital circuits encounters a fundamental difficulty: a fan-out gate cannot be generated. We resolve this missing component with the help of 2x1 particles, which can create fan-out gates that produce multiple copies of the inputs. Using these gates we provide rules for replicating arbitrary digital circuits.Comment: 27 pages, 19 figures, full version that combines three previous conference article