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Rajesh Ranjan

Publications and source records attributed to Rajesh Ranjan.

At least 19 recordsLinked to original sources

Compressibility Driven Wake Transition and Hysteresis over Cargo Aircraft Aftbodies

Aft sections of military cargo aircraft employ flat surfaces at high upsweep angles to accommodate ramp doors, producing flow features that affect cargo-drop accuracy, paratrooper safety, and aerodynamic performance. Fundamental studies have primarily examined near incompressible flow over a canonical surrogate consisting of a freestream aligned cylinder with a planar, sharp edged upswept base. The flow exhibits peripheral separation, a horseshoe vortex, and a counter-rotating streamwise vortex pair that persists downstream. The present investigation delineates the effects of compressibility on the wake and examines how these effects depend on basal upsweep angle. Wall-resolved large-eddy simulations are performed at Mach numbers of $0.1$, $0.3$, and $0.5$ for upsweep angles of $32^\circ$ and $45^\circ$ at a nominal Reynolds number of $25{,}000$. For the $32^\circ$ afterbody, increasing Mach number enlarges the upstream recirculation region and delays vortex-pair formation, while these effects diminish downstream. For the $45^\circ$ afterbody, similar recirculation-region growth triggers a bifurcation at Mach~0.5 from the vortex-pair state to a broad separated turbulent wake. A descending-Mach sequence to 0.3 and 0.1 reveals hysteresis, with the separated-wake state persisting at lower Mach numbers and remaining robust to Reynolds-number variation. Thus, both states can occur at identical Mach and Reynolds numbers, with topology and pressure loading governed by Mach number history.

physics.flu-dyn

Scaling in Supersonic Turbulence: Energy Spectra and Fluxes using High-Fidelity Direct Numerical Simulations

Supersonic turbulence is vital to astrophysical and high-speed engineering flows, yet its energy transfer mechanisms remain poorly understood. We present high-resolution ($1024^3$) direct numerical simulations (DNS) of forced compressible turbulence across a range of turbulent Mach numbers ($M_t = 0.2$ to $3.0$). Using the GPU-accelerated solver \texttt{DHARA} with a seventh-order, low-dissipation Targeted Essentially Non-Oscillatory (TENO) scheme, we resolve both fine-scale eddies and sharp shock fronts. Our results reveal a fundamental shift in the energy cascade in the supersonic regime. As $M_t$ increases, the rotational kinetic energy spectrum steepens from a Kolmogorov-like $k^{-5/3}$ scaling toward a Burgers-like $k^{-2}$ scaling. Conversely, the compressive energy spectrum becomes shallower, deviating from Burgers scaling. We show that these spectral modifications are driven by a dominant cross-scale transfer of energy from solenoidal to compressive modes within the inertial range, alongside significant contributions from pressure dilatation. Scaling laws for the root-mean-square compressive velocity ($U_C$) and compressive energy flux ($\Pi_C$) are found to mirror classical Burgers turbulence. Finally, we show that while energy injection rates depend on forcing type rather than Mach number, increased $M_t$ leads to decreased rotational dissipation and increased compressive dissipation and pressure dilatation. These findings elucidate intermodal energy cascade mechanisms, advancing our understanding of energy transfers in supersonic turbulence.

physics.flu-dyn

Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies

Fluid flows are governed by the nonlinear Navier-Stokes equations, which can manifest multiscale dynamics even from predictable initial conditions. Predicting such phenomena remains a formidable challenge in scientific machine learning, particularly regarding convergence speed, data requirements, and solution accuracy. In complex fluid flows, these challenges are exacerbated by long-range spatial dependencies arising from distant boundary conditions, which typically necessitate extensive supervision data to achieve acceptable results. We propose the Domain-Decomposed and Shifted Physics-Informed Neural Network (DDS-PINN), a framework designed to resolve such multiscale interactions with minimal supervision. By utilizing localized networks with a unified global loss, DDS-PINN captures global dependencies while maintaining local precision. The robustness of the approach is demonstrated across a suite of benchmarks, including a multiscale linear differential equation, the nonlinear Burgers' equation, and data-free Navier-Stokes simulations of flat-plate boundary layers. Finally, DDS-PINN is applied to the computationally challenging backward-facing step (BFS) problem; for laminar regimes (Re = 100), the model yields results comparable to computational fluid dynamics (CFD) without the need for any data, accurately predicting boundary layer thickness, separation, and reattachment lengths. For turbulent BFS flow at Re = 10,000, the framework achieves convergence to O(10^-4) using only 500 random supervision points (< 0.3 % of the total domain), outperforming established methods like Residual-based Attention-PINN in accuracy. This approach demonstrates strong potential for the super-resolution of complex turbulent flows from sparse experimental measurements.

physics.flu-dyn

Capability-Priced Micro-Markets: A Micro-Economic Framework for the Agentic Web over HTTP 402

This paper introduces Capability-Priced Micro-Markets (CPMM), a micro-economic framework designed to enable robust, scalable, and secure commerce among autonomous AI agents on the agentic web. The framework addresses the fundamental challenge of economic coordination in decentralized agent ecosystems, where entities must transact with minimal human oversight. CPMM synthesizes three key technologies into a unified system: MIT originated, Project NANDA infrastructure for cryptographically verifiable, capability-based security and discovery; the HTTP 402 "Payment Required" status code, with modern X402/H402 extensions for efficient, low-cost micropayments; and the Agent Capability Negotiation and Binding Protocol (ACNBP) for secure, multi-step negotiation and commitment. The paper formalizes agent interactions as a repeated bilateral game with incomplete information, demonstrating theoretically that the CPMM mechanism converges to a constrained Radner equilibrium, ensuring efficient outcomes under information asymmetry. A key theoretical contribution is the concept of "privacy elasticity of demand," which is introduced to quantify the trade-off between an agent's information disclosure and the market price of its services. By integrating secure capabilities, micropayment protocols, and formal negotiation mechanisms, CPMM provides a comprehensive, theoretically-grounded solution for creating functional micro-markets for the emergent agentic web.

cs.GT

Using the NANDA Index Architecture in Practice: An Enterprise Perspective

The proliferation of autonomous AI agents represents a paradigmatic shift from traditional web architectures toward collaborative intelligent systems requiring sophisticated mechanisms for discovery, authentication, capability verification, and secure collaboration across heterogeneous protocol environments. This paper presents a comprehensive framework addressing the fundamental infrastructure requirements for secure, trustworthy, and interoperable AI agent ecosystems. We introduce the NANDA (Networked AI Agents in a Decentralized Architecture) framework, providing global agent discovery, cryptographically verifiable capability attestation through AgentFacts, and cross-protocol interoperability across Anthropic's Modal Context Protocol (MCP), Google's Agent-to-Agent (A2A), Microsoft's NLWeb, and standard HTTPS communications. NANDA implements Zero Trust Agentic Access (ZTAA) principles, extending traditional Zero Trust Network Access (ZTNA) to address autonomous agent security challenges including capability spoofing, impersonation attacks, and sensitive data leakage. The framework defines Agent Visibility and Control (AVC) mechanisms enabling enterprise governance while maintaining operational autonomy and regulatory compliance. Our approach transforms isolated AI agents into an interconnected ecosystem of verifiable, trustworthy intelligent services, establishing foundational infrastructure for large-scale autonomous agent deployment across enterprise and consumer environments. This work addresses the critical gap between current AI agent capabilities and infrastructure requirements for secure, scalable, multi-agent collaboration, positioning the foundation for next-generation autonomous intelligent systems.

cs.NI

Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts

The Internet is poised to host billions to trillions of autonomous AI agents that negotiate, delegate, and migrate in milliseconds and workloads that will strain DNS-centred identity and discovery. In this paper, we describe the NANDA index architecture, which we envision as a means for discoverability, identifiability and authentication in the internet of AI agents. We present an architecture where a minimal lean index resolves to dynamic, cryptographically verifiable AgentFacts that supports multi-endpoint routing, load balancing, privacy-preserving access, and credentialed capability assertions. Our architecture design delivers five concrete guarantees: (1) A quilt-like index proposal that supports both NANDA-native agents as well as third party agents being discoverable via the index, (2) rapid global resolution for newly spawned AI agents, (3) sub-second revocation and key rotation, (4) schema-validated capability assertions, and (5) privacy-preserving discovery across organisational boundaries via verifiable, least-disclosure queries. We formalize the AgentFacts schema, specify a CRDT-based update protocol, and prototype adaptive resolvers. The result is a lightweight, horizontally scalable foundation that unlocks secure, trust-aware collaboration for the next generation of the Internet of AI agents, without abandoning existing web infrastructure.

cs.NI

Upgrade or Switch: Do We Need a Next-Gen Trusted Architecture for the Internet of AI Agents?

The emerging Internet of AI Agents challenges existing web infrastructure designed for human-scale, reactive interactions. Unlike traditional web resources, autonomous AI agents initiate actions, maintain persistent state, spawn sub-agents, and negotiate directly with peers: demanding millisecond-level discovery, instant credential revocation, and cryptographic behavioral proofs that exceed current DNS/PKI capabilities. This paper analyzes whether to upgrade existing infrastructure or implement purpose-built index architectures for autonomous agents. We identify critical failure points: DNS propagation (24-48 hours vs. required milliseconds), certificate revocation unable to scale to trillions of entities, and IPv4/IPv6 addressing inadequate for agent-scale routing. We evaluate three approaches: (1) Upgrade paths, (2) Switch options, (3) Hybrid index/registries. Drawing parallels to dialup-to-broadband transitions, we find that agent requirements constitute qualitative, and not incremental, changes. While upgrades offer compatibility and faster deployment, clean-slate solutions provide better performance but require longer for adoption. Our analysis suggests hybrid approaches will emerge, with centralized indexes for critical agents and federated meshes for specialized use cases.

cs.NI

LOKA Protocol: A Decentralized Framework for Trustworthy and Ethical AI Agent Ecosystems

The rise of autonomous AI agents, capable of perceiving, reasoning, and acting independently, signals a profound shift in how digital ecosystems operate, govern, and evolve. As these agents proliferate beyond centralized infrastructures, they expose foundational gaps in identity, accountability, and ethical alignment. Three critical questions emerge: Identity: Who or what is the agent? Accountability: Can its actions be verified, audited, and trusted? Ethical Consensus: Can autonomous systems reliably align with human values and prevent harmful emergent behaviors? We present the novel LOKA Protocol (Layered Orchestration for Knowledgeful Agents), a unified, systems-level architecture for building ethically governed, interoperable AI agent ecosystems. LOKA introduces a proposed Universal Agent Identity Layer (UAIL) for decentralized, verifiable identity; intent-centric communication protocols for semantic coordination across diverse agents; and a Decentralized Ethical Consensus Protocol (DECP) that could enable agents to make context-aware decisions grounded in shared ethical baselines. Anchored in emerging standards such as Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and post-quantum cryptography, LOKA proposes a scalable, future-resilient blueprint for multi-agent AI governance. By embedding identity, trust, and ethics into the protocol layer itself, LOKA proposes the foundation for a new era of responsible, transparent, and autonomous AI ecosystems operating across digital and physical domains.

cs.MA

Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System

Ensuring fairness in decentralized multi-agent systems presents significant challenges due to emergent biases, systemic inefficiencies, and conflicting agent incentives. This paper provides a comprehensive survey of fairness in multi-agent AI, introducing a novel framework where fairness is treated as a dynamic, emergent property of agent interactions. The framework integrates fairness constraints, bias mitigation strategies, and incentive mechanisms to align autonomous agent behaviors with societal values while balancing efficiency and robustness. Through empirical validation, we demonstrate that incorporating fairness constraints results in more equitable decision-making. This work bridges the gap between AI ethics and system design, offering a foundation for accountable, transparent, and socially responsible multi-agent AI systems.

cs.MA

Gender Biases in LLMs: Higher intelligence in LLM does not necessarily solve gender bias and stereotyping

Large Language Models (LLMs) are finding applications in all aspects of life, but their susceptibility to biases, particularly gender stereotyping, raises ethical concerns. This study introduces a novel methodology, a persona-based framework, and a unisex name methodology to investigate whether higher-intelligence LLMs reduce such biases. We analyzed 1400 personas generated by two prominent LLMs, revealing that systematic biases persist even in LLMs with higher intelligence and reasoning capabilities. o1 rated males higher in competency (8.1) compared to females (7.9) and non-binary (7.80). The analysis reveals persistent stereotyping across fields like engineering, data, and technology, where the presence of males dominates. Conversely, fields like design, art, and marketing show a stronger presence of females, reinforcing societal notions that associate creativity and communication with females. This paper suggests future directions to mitigate such gender bias, reinforcing the need for further research to reduce biases and create equitable AI models.

cs.CY

Comprehensive Framework for Evaluating Conversational AI Chatbots

Conversational AI chatbots are transforming industries by streamlining customer service, automating transactions, and enhancing user engagement. However, evaluating these systems remains a challenge, particularly in financial services, where compliance, user trust, and operational efficiency are critical. This paper introduces a novel evaluation framework that systematically assesses chatbots across four dimensions: cognitive and conversational intelligence, user experience, operational efficiency, and ethical and regulatory compliance. By integrating advanced AI methodologies with financial regulations, the framework bridges theoretical foundations and real-world deployment challenges. Additionally, we outline future research directions, emphasizing improvements in conversational coherence, real-time adaptability, and fairness.

cs.CY

A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions

This paper presents a comprehensive study of Retrieval-Augmented Generation (RAG), tracing its evolution from foundational concepts to the current state of the art. RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs, addressing key limitations of LLMs. The study explores the basic architecture of RAG, focusing on how retrieval and generation are integrated to handle knowledge-intensive tasks. A detailed review of the significant technological advancements in RAG is provided, including key innovations in retrieval-augmented language models and applications across various domains such as question-answering, summarization, and knowledge-based tasks. Recent research breakthroughs are discussed, highlighting novel methods for improving retrieval efficiency. Furthermore, the paper examines ongoing challenges such as scalability, bias, and ethical concerns in deployment. Future research directions are proposed, focusing on improving the robustness of RAG models, expanding the scope of application of RAG models, and addressing societal implications. This survey aims to serve as a foundational resource for researchers and practitioners in understanding the potential of RAG and its trajectory in natural language processing.

cs.CL

A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Large Language Models(LLMs) have revolutionized various applications in natural language processing (NLP) by providing unprecedented text generation, translation, and comprehension capabilities. However, their widespread deployment has brought to light significant concerns regarding biases embedded within these models. This paper presents a comprehensive survey of biases in LLMs, aiming to provide an extensive review of the types, sources, impacts, and mitigation strategies related to these biases. We systematically categorize biases into several dimensions. Our survey synthesizes current research findings and discusses the implications of biases in real-world applications. Additionally, we critically assess existing bias mitigation techniques and propose future research directions to enhance fairness and equity in LLMs. This survey serves as a foundational resource for researchers, practitioners, and policymakers concerned with addressing and understanding biases in LLMs.

cs.CL

Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system

This paper provides a comprehensive survey of sentiment analysis within the context of artificial intelligence (AI) and large language models (LLMs). Sentiment analysis, a critical aspect of natural language processing (NLP), has evolved significantly from traditional rule-based methods to advanced deep learning techniques. This study examines the historical development of sentiment analysis, highlighting the transition from lexicon-based and pattern-based approaches to more sophisticated machine learning and deep learning models. Key challenges are discussed, including handling bilingual texts, detecting sarcasm, and addressing biases. The paper reviews state-of-the-art approaches, identifies emerging trends, and outlines future research directions to advance the field. By synthesizing current methodologies and exploring future opportunities, this survey aims to understand sentiment analysis in the AI and LLM context thoroughly.

cs.CL

Evaluation of LLMs Biases Towards Elite Universities: A Persona-Based Exploration

This study investigates whether popular LLMs exhibit bias towards elite universities when generating personas for technology industry professionals. We employed a novel persona-based approach to compare the educational background predictions of GPT-3.5, Gemini, and Claude 3 Sonnet with actual data from LinkedIn. The study focused on various roles at Microsoft, Meta, and Google, including VP Product, Director of Engineering, and Software Engineer. We generated 432 personas across the three LLMs and analyzed the frequency of elite universities (Stanford, MIT, UC Berkeley, and Harvard) in these personas compared to LinkedIn data. Results showed that LLMs significantly overrepresented elite universities, featuring these universities 72.45% of the time, compared to only 8.56% in the actual LinkedIn data. ChatGPT 3.5 exhibited the highest bias, followed by Claude Sonnet 3, while Gemini performed best. This research highlights the need to address educational bias in LLMs and suggests strategies for mitigating such biases in AI-driven recruitment processes.

cs.CY

Global stability analysis of flow behind an upswept aftbody

Wakes of aircraft and automobiles with relatively flat slanted aftbodies are often characterized by a streamwise-oriented vortex pair, whose strength affects drag and other crucial performance parameters. We examine the stability characteristics of the vortex pair emerging over an abstraction comprised of a streamwise-aligned cylinder terminated with an upswept plane. The Reynolds number is fixed at 5000 and the upsweep angle is increased from 20deg to 32deg. At 20deg, the LES yields a steady streamwise-oriented vortex pair, and the global modes are also stable. At 32deg, the LES displays unsteady flow behavior. Linear analysis of the mean flow reveals different unstable modes. The lowest oscillation frequency is an antisymmetric mode, which is attached to the entire slanted base. At the highest frequency, the mode is symmetric and has the same rotational orientation as the mean vortex pair. Its support is prominent in the rear part of the slanted base and spreads relatively rapidly downstream with prominent helical structures. A receptivity analysis of low- and high-frequency modes suggests the latter holds promise to affect the vortical flow, providing a potential starting point for a control strategy to modify the vortex pair.

physics.flu-dyn

Meandering dynamics of streamwise vortex pairs in afterbody wakes

Wakes of upswept afterbodies are often characterized by a counter-rotating streamwise vortex pair. The unsteady dynamics of these vortices are examined with a spatio-temporally resolved Large-Eddy Simulation dataset on a representative configuration consisting of a cylinder with an upswept basal surface. Emphasis is placed on understanding the meandering motion of the vortices in the pair, including vortex core displacement, spectral content, stability mechanisms and overall rank-behavior. The first two energy-ranked modes obtained through Proper Orthogonal Decomposition(POD) of the time-resolved vorticity field reveals a pair of vortex dipoles aligned relatively perpendicularly to each other. The dynamics is successfully mapped to a matched Batchelor vortex pair whose spatial and temporal stability analyses indicate similar dipole structures associated with an |m|=1 elliptic mode pair. This short-wave elliptic instability dominates the meandering motion, with strain due to axial velocity playing a key role in breakdown. The low frequency of the unstable mode (Strouhal number StD =0.3 based on cylinder diameter) is consistent with spectral analysis of meandering in the LES. The wake is examined for its rank behavior; the number of modes required to reproduce the flow to given degree of accuracy diminishes rapidly outside of the immediate vicinity of the base. Beyond two diameters downstream, only two leading POD modes are required to reconstruct the dominant meandering motion and spatial structure in the LES data with < 15% performance loss, while ten modes nearly completely recover the flow field. This low-rank behavior may hold promise in constructing a reduced-order model for control purposes.

physics.flu-dyn

Prediction of separation and transition on a low-pressure turbine blade using a RANS grid

Flow past a high-lift low-pressure turbine (LPT) blade in a cascade could be quite complex as phenomena like separation and transition are often involved. For a highly loadedT106A blade at a high incidence and relatively low Reynolds number(25, 000 < Re < 1, 00, 000), separation-induced transition is observed on the suction side of the blade, making it a challenging problem for model-based simulations. In this work, computations for this flow are carried out using RANS and hybrid LES/RANS approaches. The RANS simulations are performed with six popular low- Re turbulence models. While turbulence models by themselves fail to predict any separation on the T106A blade, the four-equation Langtry-Menter transition model predicts a short separation bubble. The characteristic of this bubble, however, is very different from what is observed in experiments and DNS, and therefore transition is not accurately predicted. An embedded hybrid LES/RANS approach, Limited numerical scales(LNS), with an automatic switch to LES in sufficiently resolved grids, is then used for predictions on the sameRANS grid. With the statistical turbulence on fine grids, LES-like behavior of LNS results in an unphysical drop in Reynolds stresses as the turbulent fluctuations are not appropriately represented on the resolved scale. Therefore, the LNS results are very similar to those obtained with turbulence models. However, when synthetic turbulence with correct statistical characteristics is used to stimulate the large eddies in the embedded LES zone, LNS is able to predict separation and recovers a solution very close to DNS and experimental results.

physics.flu-dyn