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eshing wind/tidal turbine using Turbogrid

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这篇文章主要围绕eshingwind/tidalturbineusingTurbogrid展开,旨在为您提供一份详细的参考资料。我们将全面介绍eshingwind/tidalturbineusingT

这篇文章主要围绕eshing wind/tidal turbine using Turbogrid展开,旨在为您提供一份详细的参考资料。我们将全面介绍eshing wind/tidal turbine using Turbogrid,同时也会为您带来Capturing and validating alphanumeric identifiers in Amazon Lex、ch.qos.logback.classic.turbo.TurboFilter的实例源码、Hystrix和Turbine不适用于Spring Boot 2和Spring Cloud Finchley.M8、OpenAI 为 GPT-3.5 Turbo 推出微调功能 (fine-tuning)的实用方法。

本文目录一览:

eshing wind/tidal turbine using Turbogrid

eshing wind/tidal turbine using Turbogrid

 

 

Table of Contents

  • 1. meshing wind turbine using Turbogrid
    • 1.1. ref

1 meshing wind turbine using Turbogrid

Geo: solidworks Mesh: Turbogrid

Typically Features of wind turbine blades

  • highly twisted
  • no shroud.

So this cannot be meshed in TurboGrid.

  1. The best option is to use ICEM CFD Hexa to generate a grid for such blades.
  2. One way to overcome this is to split the blade near the tip,so that you put an artificial shroud.

This can then be mesh in TurboGrid. As for the tip section you can generate a mesh in ICEM CFD Hexa or tetra and use ggi‘s to connect the two meshes in CFX Pre.

Limits

the shroud must be defined near the tip of blade but in the wind turbine study we need a region between tip and shroud for this we cannot use turbogrid

  1. GGI option for shroud can be used and has been used by ppl to simulate wind turbines using turbo-grid meshing.
  2. For Blade Profile deFinition you just need the co-ordinates. i. e.

If you want to rotate a blade about x. axis,define the airfoils in X-Y planes and vary Z co-ordinate. I myself have done this and my blade was twisted as well.

  1. Cartesian co-ordinates are enough,no need to enter cylindrical co-ordinates.

1.1 ref

https://www.cfd-online.com/Forums/cfx/22798-can-i-use-turbogrid-mesh-wind-turbine-blade.html https://www.cfd-online.com/Forums/ansys-meshing/141898-water-turbine-meshed-turbogrid-geometry-topology-issues.html

Created: 2019-05-12 周日 05:59

Emacs 25.3.1 (Org mode 8.2.10)

Validate

Capturing and validating alphanumeric identifiers in Amazon Lex

Capturing and validating alphanumeric identifiers in Amazon Lex

https://amazonaws-china.com/blogs/machine-learning/capturing-and-validating-alphanumeric-identifiers-in-amazon-lex/


Enterprises often rely on unique identifiers to look up information on accounts or events. For example, airlines use confirmation codes to locate itineraries, and insurance companies use policy IDs to retrieve policy details. In customer support, these identifiers are the first level of information necessary to address customer requests. Identifiers are typically a combination of letters and numbers and have a specific pattern. It is likely a fixed length with specific positions of letters and numbers. Previously, you had to create a custom slot type with a broad range of applicable values. As of this writing, you can use AMAZON.AlphaNumeric slot type in Amazon Lex to capture such inputs in your bot. This slot type can capture combinations of letters and numbers. You can extend this slot type by applying a validation check to create a custom slot type. You can apply these validation checks by specifying a regular expression (regex) on the AMAZON.AlphaNumeric slot type. This post demonstrates how to use the AMAZON.AlphaNumeric slot type to capture alphanumeric information and restrict such input to a specific pattern.

Building a bot

This post uses the following conversation to model a bot:

User: I want to reschedule my flight.
Agent: Sure. Can you provide the confirmation code?
User: ABC12D
Agent: Thank you. I see you are on the 6 AM flight to Seattle tomorrow. What would you like to change to?
User: I want to change to next Monday.
Agent: What time would you prefer to travel?
User: 8 AM.
Agent: Sure. Let me make that change. For authentication purposes, can I get the last four digits of the credit card number?
User: Yes, it’s 1234.
Agent: Excellent. I have rescheduled you for the flight departing on February 10 at 8 AM. Anything else I can help you with?
User: No.
Agent: Thank you. Have a great day!










First, build an Amazon Lex bot (FlightReservationBot) with the following intents:

  • MakeReservation – Elicits the travel date, source, and destination cities to process a reservation and return a booking confirmation code.
  • ModifyReservation – Elicits the confirmation code and last four digits of the user’s credit card number to modify the reservation.
  • CancelReservation – Elicits the confirmation code to cancel the reservation.

You can create the built-in slot type AMAZON.AlphaNumeric. Such a design helps capture alphanumeric information but doesn’t validate it. To validate, use a regular expression. Create a new slot type ConfirmationCode by extending AMAZON.AlphaNumeric. The confirmation code has a fixed format of six characters: [letter] [letter][letter][number][number][letter]. For example, ABC12D is a confirmation code.

To capture the confirmation code and enforce the necessary validation checks, complete the following steps:

  1. On the Amazon Lex console, choose FlightReservationBot.
  2. Next to Slot types, choose the plus sign.
  3. Choose Extend slot type.

  4. For Slot type name, enter ConfirmationCode.
  5. For Description, enter a description of your slot type.
  6. For Regular expression, restrict the slot type to the six-character fixed format as specified previously by entering the expression [A-D]{1}[A-Z]{2}[0-9]{2}[A-Z]{1}.
  7. Choose Save slot type.

You can now use the ConfirmationCode slot type to design the ModifyReservation and CancelReservation intents for your bot and make sure that the user input contains a valid confirmation code.

To add the ConfirmationCode slot type to the ModifyReservation intent, complete the following steps:

  1. On the Amazon Lex console, choose FlightReservationBot.
  2. Under Intents, choose
  3. Under Slots, add a new slot BookingConfirmationCode with Slot type ConfirmationCode with an appropriate prompt.
  4. Design the rest of the bot as per your use-case.
  5. Choose Save Intent, Build, and Publish.

Your bot is now ready to use. You can test it by providing a confirmation code such as ABCDE that does not match the pattern specified by the regular expression.

Conclusion

This post demonstrated how to capture alphanumeric values using the AMAZON.AlphaNumeric slot type. You can further restrict the slot values captured to a fixed format by providing a regular expression. For more information please refer to the Amazon Lex documentation. Try out the AlphaNumeric slot type in your bot today!


About the author

Anubhav Mishra is a Product Manager with AWS. He spends his time understanding customers and designing product experiences to address their business challenges.

 

 

 

ch.qos.logback.classic.turbo.TurboFilter的实例源码

ch.qos.logback.classic.turbo.TurboFilter的实例源码

项目:scylla-tools-java    文件:AggregationTest.java   
private static void configureLogbackScanPeriod(long millis)
{
    Logger l = LoggerFactory.getLogger(AggregationTest.class);
    ch.qos.logback.classic.Logger logbackLogger = (ch.qos.logback.classic.Logger) l;
    LoggerContext ctx = logbackLogger.getLoggerContext();
    TurboFilterList turboFilterList = ctx.getTurboFilterList();
    boolean done = false;
    for (TurboFilter turboFilter : turboFilterList)
    {
        if (turboFilter instanceof ReconfigureOnChangeFilter)
        {
            ReconfigureOnChangeFilter reconfigureFilter = (ReconfigureOnChangeFilter) turboFilter;
            reconfigureFilter.setRefreshPeriod(millis);
            reconfigureFilter.stop();
            reconfigureFilter.start(); // start() sets the next check timestammp
            done = true;
            break;
        }
    }
    assertTrue("ReconfigureOnChangeFilter not in logback's turbo-filter list - do that by adding scan=\"true\" to logback-test.xml's configuration element",done);
}
项目:spring-webmvc-dynamiclogging    文件:LoggerFilterController.java   
@RequestMapping(value = "/1.0/admin/logging/logger/_add",method = RequestMethod.GET)
public @ResponseBody
Object addFilter(@NotEmpty(message = "You must specify a 'logger' param") @RequestParam(required = false) String logger,@RequestParam(required = false,defaultValue = "trace") String level)
{
    LoggerContext context = (LoggerContext) LoggerFactory.getILoggerFactory();
    for(TurboFilter filter : context.getTurboFilterList())
    {
        if(DynamicLoggerFilter.class.equals(filter.getClass()))
        {
            for(Logger l : context.getLoggerList())
            {
                if(l.getName().endsWith(logger))
                    ((DynamicLoggerFilter)filter).addOverride(l,level);
            }
            return ((DynamicLoggerFilter)filter).getRegisteredFilters();
        }
    }
    return "DynamicLoggerFilter not configured.  Please check logback.xml.";
}
项目:spring-webmvc-dynamiclogging    文件:LoggerFilterController.java   
@RequestMapping(value = "/1.0/admin/logging/logger/_drop",method = RequestMethod.GET)
public @ResponseBody
Object dropFilter(@NotEmpty(message = "You must specify a 'logger' param") @RequestParam(required = false) String logger)
{
    LoggerContext context = (LoggerContext) LoggerFactory.getILoggerFactory();
    for(TurboFilter filter : context.getTurboFilterList())
    {
        if(DynamicLoggerFilter.class.equals(filter.getClass()))
        {
            for(Logger l : context.getLoggerList())
            {
                if(l.getName().equals(logger))
                    ((DynamicLoggerFilter)filter).dropOverride(l);
            }
            return ((DynamicLoggerFilter)filter).getRegisteredFilters();
        }
    }
    return "DynamicLoggerFilter not configured.  Please check logback.xml.";
}
项目:konker-platform    文件:KonkerLoggerContext.java   
public void resetTurboFilterList() {
    Iterator i$ = this.turboFilterList.iterator();

    while(i$.hasNext()) {
        TurboFilter tf = (TurboFilter)i$.next();
        tf.stop();
    }

    this.turboFilterList.clear();
}
项目:uaa-service    文件:TraceThreadLoggingInitializer.java   
@EventListener
public void handleContextRefresh(final ContextRefreshedEvent event) {
    final LoggerContext loggerContext = ((Logger) LoggerFactory.getLogger("")).getLoggerContext();
    loggerContext.addTurboFilter(new TurboFilter() {
        @Override
        public FilterReply decide(final Marker marker,final Logger logger,final Level level,final String format,final Object[] params,final Throwable t) {
            return TraceThreadLoggingSupport.isTraceActive() ? FilterReply.ACCEPT : FilterReply.NEUTRAL;
        }
    });
    LOGGER.info("ThreadLogging support initialized");
}
项目:bartleby    文件:LoggerContext.java   
/**
 * First processpriorToRemoval all registered turbo filters and then clear the registration
 * list.
 */
public void resetTurboFilterList() {
  for (TurboFilter tf : turboFilterList) {
    tf.stop();
  }
  turboFilterList.clear();
}
项目:bartleby    文件:JoranConfiguratorTest.java   
@Test
public void turboFilter() throws JoranException {
  // Although this test uses turbo filters,it only checks
  // that Joran can see the xml element and create
  // and place the relevant object correctly.
  configure(ClassicTestConstants.JORAN_INPUT_PREFIX + "turbo.xml");

  TurboFilter filter = loggerContext.getTurboFilterList().get(0);
  assertTrue(filter instanceof nopTurboFilter);
}
项目:bartleby    文件:JoranConfiguratorTest.java   
@Test
public void testTurboFilterWithStringList() throws JoranException {
  // Although this test uses turbo filters,it only checks
  // that Joran can see <user> elements,and behave correctly
  // that is call the addUser method and pass the correct values
  // to that method.
  configure(ClassicTestConstants.JORAN_INPUT_PREFIX + "turbo2.xml");

  // StatusPrinter.print(loggerContext.getStatusManager());

  TurboFilter filter = loggerContext.getTurboFilterList().get(0);
  assertTrue(filter instanceof DebugUsersTurboFilter);
  DebugUsersTurboFilter dutf = (DebugUsersTurboFilter) filter;
  assertEquals(2,dutf.getUsers().size());
}
项目:spring-webmvc-dynamiclogging    文件:MDCFilterController.java   
@RequestMapping(value = "/1.0/admin/logging/mdc",method = RequestMethod.GET)
public @ResponseBody
Object getFilters()
{
    LoggerContext context = (LoggerContext) LoggerFactory.getILoggerFactory();
    for (TurboFilter filter : context.getTurboFilterList())
    {
        if (DynamicMDCFilter.class.equals(filter.getClass()))
        {
            return ((DynamicMDCFilter) filter).getRegisteredFilters();
        }
    }
    return "DynamicMDCFilter not configured.  Please check logback.xml.";
}
项目:spring-webmvc-dynamiclogging    文件:MDCFilterController.java   
@RequestMapping(value = "/1.0/admin/logging/mdc/_dropAll",method = RequestMethod.GET)
public @ResponseBody
Object dropAllFilter()
{
    LoggerContext context = (LoggerContext) LoggerFactory.getILoggerFactory();
    for(TurboFilter filter : context.getTurboFilterList())
    {
        if(DynamicMDCFilter.class.equals(filter.getClass()))
        {
            ((DynamicMDCFilter)filter).clear();
            return ((DynamicMDCFilter)filter).getRegisteredFilters();
        }
    }
    return "DynamicMDCFilter not configured.  Please check logback.xml.";
}
项目:spring-webmvc-dynamiclogging    文件:LoggerFilterController.java   
@RequestMapping(value = "/1.0/admin/logging/logger",method = RequestMethod.GET)
public @ResponseBody
Object getFilters()
{
    LoggerContext context = (LoggerContext) LoggerFactory.getILoggerFactory();
    for (TurboFilter filter : context.getTurboFilterList())
    {
        if (DynamicLoggerFilter.class.equals(filter.getClass()))
        {
            return ((DynamicLoggerFilter) filter).getRegisteredFilters();
        }
    }
    return "DynamicLoggerFilter not configured.  Please check logback.xml.";
}
项目:spring-webmvc-dynamiclogging    文件:LoggerFilterController.java   
@RequestMapping(value = "/1.0/admin/logging/logger/_dropAll",method = RequestMethod.GET)
public @ResponseBody
Object dropAllFilter()
{
    LoggerContext context = (LoggerContext) LoggerFactory.getILoggerFactory();
    for(TurboFilter filter : context.getTurboFilterList())
    {
        if(DynamicLoggerFilter.class.equals(filter.getClass()))
        {
            ((DynamicLoggerFilter)filter).clear();
            return ((DynamicLoggerFilter)filter).getRegisteredFilters();
        }
    }
    return "DynamicLoggerFilter not configured.  Please check logback.xml.";
}
项目:bartleby    文件:LoggerContext.java   
public void addTurboFilter(TurboFilter newFilter) {
  turboFilterList.add(newFilter);
}

Hystrix和Turbine不适用于Spring Boot 2和Spring Cloud Finchley.M8

Hystrix和Turbine不适用于Spring Boot 2和Spring Cloud Finchley.M8

我在Spring Boot 2和最新版本的Spring Cloud上尝试了turbo +
hystrix仪表板,似乎存在一些问题,并且turbo无法从响应服务获取数据流。我刚刚将简单的微服务上传到github

https://github.com/armdev/reactive-spring-
cloud

像这样的异常:

com.netflix.turbine.monitor.instance.InstanceMonitor$MisconfiguredHostException: [{"timestamp":"2018-03-08T17:22:05.809+0000","status":404,"error":"Not Found","message":"No message available","path":"/hystrix.stream"}]
    at com.netflix.turbine.monitor.instance.InstanceMonitor.init(InstanceMonitor.java:318) ~[turbine-core-1.0.0.jar:na]
    at com.netflix.turbine.monitor.instance.InstanceMonitor.access$100(InstanceMonitor.java:103) ~[turbine-core-1.0.0.jar:na]
    at com.netflix.turbine.monitor.instance.InstanceMonitor$2.call(InstanceMonitor.java:235) [turbine-core-1.0.0.jar:na]
    at com.netflix.turbine.monitor.instance.InstanceMonitor$2.call(InstanceMonitor.java:229) [turbine-core-1.0.0.jar:na]
    at java.util.concurrent.FutureTask.run(FutureTask.java:266) [na:1.8.0_101]
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) [na:1.8.0_101]
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) [na:1.8.0_101]
    at java.lang.Thread.run(Thread.java:745) [na:1.8.0_101]

或损坏的PIPE。

有人尝试过使用Spring Webflux的完整Spring Cloud堆栈吗?有什么建议么?

OpenAI 为 GPT-3.5 Turbo 推出微调功能 (fine-tuning)

OpenAI 为 GPT-3.5 Turbo 推出微调功能 (fine-tuning)

OpenAI 宣布为 GPT-3.5 Turbo 推出微调功能 (fine-tuning)。

微调允许用户可以在自己的数据上训练模型并大规模运行。OpenAI 称早期测试表明,经过微调的 GPT-3.5 Turbo 可以在狭窄任务上与 GPT-4 相匹敌或超越 GPT-4。

关键点:

  1. GPT-3.5 API 的微调:OpenAI 现在允许开发者对 GPT-3.5 API 进行微调,这意味着开发者可以使用自己的数据来定制模型,使其更适合特定的应用或风格。
  2. 使用问答对进行微调:开发者可以通过提供一系列的问答对来进行微调,这样可以训练出具有特定功能或风格的模型。
  3. Prompt 的优化:OpenAI 提供了一种方法,可以将 Prompt 嵌入到模型中,从而减少 Prompt 的长度和占用。这可以优化最多 90% 的 Prompt 占用,从而节省 API 调用的成本。
  4. API 价格的变化:与微调功能的推出相伴随的是 API 价格的增加。微调后的模型使用费用是原来的 9 倍,但仍然比 GPT-4 的价格便宜。

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