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Derek Warner

Publications and source records attributed to Derek Warner.

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Precipitate-Induced Dynamic Strain Aging and Its Effect on the Strain Rate Sensitivity of Precipitation Hardened Aluminum Alloys

We examine precipitate-induced dynamic strain aging in precipitation-hardened Al-Cu alloys by combining atomistic simulations, kinetic Monte Carlo, and analytical rate theory. Atomistic simulations were used to characterize (1) the energetics of nearest neighbour Cu<->Al exchanges at dislocation - precipitate junctions and (2) the subsequent change in obstacle strength. For robustness, the simulations were performed with two distinct interatomic potentials. The resulting catalog of local Cu-Al exchange events was used as input for a kinetic Monte Carlo model of the time-dependent evolution of obstacle strength during dislocation pinning at the precipitate. The predicted strengthening kinetics were then embedded in an analytical dynamic strain aging model to predict the strain-rate sensitivity parameter. On the whole, the modeling predicts a low strain-rate sensitivity across a broad range of intermediate quasi-static strain rates, consistent with experimental observations for precipitate-strengthened alloys. The results therefore identify a mechanistic origin of the low strain-rate sensitivity in precipitation hardened aluminum alloys, emerging directly from the kinetics of dislocation-precipitate interactions when nearest neighbour Cu<->Al exchanges are considered.

cond-mat.mtrl-sci

MojoFrame: Dataframe Library in Mojo Language

Mojo is an emerging programming language built on MLIR (Multi-Level Intermediate Representation) and supports JIT (Just-in-Time) compilation. It enables transparent hardware-specific optimizations (e.g., for CPUs and GPUs), while allowing users to express their logic using Python-like user-friendly syntax. Mojo has demonstrated strong performance on tensor operations; however, its capabilities for relational operations (e.g., filtering, join, and group-by aggregation) common in data science workflows, remain unexplored. To date, no dataframe implementation exists in the Mojo ecosystem. In this paper, we introduce the first Mojo-native dataframe library, called MojoFrame, that supports core relational operations and user-defined functions (UDFs). MojoFrame is built on top of Mojo's tensor to achieve fast operations on numeric columns, while utilizing a cardinality-aware approach to effectively integrate non-numeric columns for flexible data representation. To achieve high efficiency, MojoFrame takes significantly different approaches than existing libraries. We show that MojoFrame supports all operations for TPC-H queries and a selection of TPC-DS queries with promising performance, achieving up to 4.60x speedup versus existing dataframe libraries in other programming languages. Nevertheless, there remain optimization opportunities for MojoFrame (and the Mojo language), particularly in in-memory data representation and dictionary operations.

cs.DB

Assessment of ChatGPT for Engineering Statics Analysis

Large language models (LLMs) such as OpenAI's ChatGPT hold potential for automating engineering analysis, yet their reliability in solving multi-step statics problems remains uncertain. This study evaluates the performance of ChatGPT-4o and ChatGPT-o1-preview on foundational statics tasks, from simple calculations of Newton's second law of motion to beam and truss analyses and compares their results to first-year engineering students on a typical statics exam. To enhance accuracy, we developed a Custom GPT, embedding refined prompts directly into its instructions. This optimized model achieved an 82% score, surpassing the 75% student average, demonstrating the impact of tailored guidance. Despite these improvements, LLMs continued to exhibit errors in nuanced or open-ended problems, such as misidentifying tension and compression in truss members. These findings highlight both the promise and current limitations of AI in structural analysis, emphasizing the need for improved reasoning, multimodal capabilities, and targeted training data for future AI-driven automation in civil and mechanical engineering.

cs.CE