1. 起源与历史背景

2. 基本定义

3. 边界条件与载荷

4. 求解方程

5. 输出和结果



1. 离散化

2. 建模

3. 方程组装

4. 应用边界条件与载荷

5. 求解



1. 结构分析

2. 热分析

3. 流体力学分析

4. 电磁分析

5. 耦合场分析

6. 生物医学工程应用

7. 地下和岩土工程



1. 设计优化

2. 复杂系统模拟

3. 风险和成本降低

4. 快速原型制作

5. 提高安全性

6. 耦合现象的研究



1. 需要专业知识

2. 计算时间和资源

3. “垃圾进,垃圾出”

4. 模型简化可能导致不准确性

5. 对初始条件和边界条件敏感

6. 无法完全替代实验测试



1. 复杂性和学习曲线

2. 网格生成和优化

3. 计算资源限制

4. 模型验证与校准

5. 非线性和复杂物理现象

6. 软件的限制

7. 敏感性分析



ANSYS 是一款功能强大的工程仿真软件,涵盖结构、流体、电磁、声学等多种物理场仿真。

特点:
应用示例:


特点:
应用示例:


特点:
应用示例:


特点:
应用示例:


特点:
应用示例:


特点:
应用示例:

import requests, re, json; from bs4 import BeautifulSoup; def fetch_google_scholar(query, num_results=10): headers = {"User-Agent": "Mozilla/5.0"}; url = f"https://scholar.google.com/scholar?q={query}"; response = requests.get(url, headers=headers); soup = BeautifulSoup(response.text, 'html.parser'); results = []; for result in soup.find_all('div', class_="gs_ri")[:num_results]: title = result.find('h3').text; link = result.find('h3').find('a')['href'] if result.find('h3').find('a') else None; snippet = result.find('div', class_="gs_rs").text; results.append({"title": title, "link": link, "snippet": snippet}); return results; def fetch_journal_papers(journal_url, num_papers=10): headers = {"User-Agent": "Mozilla/5.0"}; response = requests.get(journal_url, headers=headers); soup = BeautifulSoup(response.text, 'html.parser'); papers = []; for paper in soup.find_all('div', class_="paper-title")[:num_papers]: title = paper.text; link = paper.find('a')['href']; papers.append({"title": title, "link": link}); return papers; def categorize_papers(papers, categories): categorized = {category: [] for category in categories}; for paper in papers: for category in categories: if re.search(category, paper['title'], re.IGNORECASE): categorized[category].append(paper); return categorized; def summarize_paper_titles(papers): summary = "Summary of Papers:\n"; for paper in papers: summary += f"- {paper['title']}\n"; return summary; papers_google = fetch_google_scholar("machine learning", 5); papers_journal = fetch_journal_papers("https://journal.com/latest", 5); combined_papers = papers_google + papers_journal; categories = ["deep learning", "neural networks", "reinforcement learning"]; categorized_papers = categorize_papers(combined_papers, categories); print(json.dumps(categorized_papers, indent=2)); print(summarize_paper_titles(combined_papers))