Hi Every Energy Model Is Wrong—And Here Is Why They Are Indispensable.

Hi Every Energy Model Is Wrong—And Here Is Why They Are Indispensable.

Recently, LEED has come under fire for accounts of certified buildings not performing as well as their energy models predicted. Frequently mentioned amongst the antagonistic “gotcha” coverage is an out-of-context 2007 quote by the USGBC Research Committee acknowledging: “Buildings have a poor track record of performing as predicted during design.”

Within context, the research committee clarifies the reasons for the frequency of underperforming energy models, citing “inaccurate or improperly used analysis tools, lack of integration of complex interconnected systems, value engineering after design, poor construction practices, no building commissioning, and incomplete or improper understanding of operations and maintenance practices.” Not nearly an exhaustive list, but all legitimate considerations.

Energy models will continue to become more accurate as the market develops and methodologies and software become more robust and sophisticated. Models can be calibrated based on actual performance data to further increase their accuracy for measurement and verification purposes. But let’s be clear—to some degree, all energy models are wrong. They always will be. At first blush, one may reasonably presuppose energy models are based solely on physics and, as such, they should be extremely precise—perhaps 95 to 99 percent accurate. Yet all building energy models also require inputs based on assumptions and long-term trends. We cannot predict the future—e.g., abnormal weather patterns, mechanical malfunctions, changes in occupancy, occupant behavior—but all of these factors have a chaotic effect on performance outcomes.

Nevertheless, energy modeling is essential for any high-performance building project—no matter how big or small. Energy models facilitate sustainable design in three essential ways:

1. To Understand. Energy models allow us to understand more about how our buildings are likely to perform. They allow design teams to test hypotheses and simulate field conditions for both proposed designs and existing structures. I was once approached to advise on dripping water in the ceiling of a museum. Through energy modeling—specifically a hygrothermal (i.e., pertaining to both humidity and temperature) analysis—it was determined that an ill-advised vapor retarder was preventing vapor drive toward the exterior. Add seasonal temperature extremes and high interior relative humidity, and it was a recipe for condensation.

2. To Compare. It is easier (and much less expensive) to experiment in the computer than in real life. Energy models are most valuable during the earlier stages of the design process when their results can help guide decision-making. As a parametric design tool, energy models can be used to evaluate everything from conceptual massing options to different glass types. This is the very premise of the “simple box” energy modeling analysis within the LEED v4 integrative process credit—and there is an abundance of user-friendly software platforms currently available in the market, many of them free. This kind of early-stage design performance modeling allows design teams to go beyond rules of thumb to actually fine-tune environmental control systems and energy conservation measures.

3. To Forecast. Buildings are investments, and the separation between construction capital and operating expenses makes it difficult to finance long-term improvements in building performance. Energy models improve our insight of the connections between—and business-case benefits of—various building systems in relation to high-performance outcomes. Despite a certain degree of imprecision, energy models can be leveraged to forecast the return on investment in high-performance building upgrades, such as onsite renewable energy, automated exterior louver systems or even that extra inch of rigid insulation on the roof. More frequently, project teams are using energy models to anticipate the order of magnitude to which future climate change could impact the economics of building performance, operations and maintenance.

In a recent TED talk, climate modeler Gavin A. Schmidt, director of the NASA Goddard Institute for Space Studies, insisted, “Models are not right or wrong; they’re always wrong. They’re always approximations. The question you have to ask is whether a model tells you more information than you would have had otherwise.”

Energy models are not meant to predict the future. They are powerful tools that enable us to better understand the behavior of our structures, fine-tune building systems and strategies, and forecast future performance trends. 

Hi Ozone Layer Healing, But...

Hi Ozone Layer Healing, But...

According to the ozone sensors on Europe's MetOpt weather satellite, the hole over Antarctica in 2012 was the smallest in the last 10 years. 

Since the beginning of the 1980s, an ozone hole has developed over Antarctica during the southern spring - September to November - resulting in a decrease in ozone concentration of up to 70%. 

Man-made chlorofluorocarbons - CFCs - have a negative effect on the ozone, depleting it and creating the infamous hole. 

The Montreal Protocol has stopped the increase of CFC concentrations, and a drastic fall has been observed since the mid-1990s. 

You can see the Total Ozone values in the Northern Hemishpere and Southern Hemisphere along with the climate model prediction (in blue) showing recovery continuing over several decades.

The good news is that the Antarctic ozone hole is on the way to recovery. 

The bad news is that scientists now think it is helping to slow the polar seas' ability to absorb carbon dioxide, a leading contributor to Global Warming. 

Antarctic accounts for about 40% of the total carbon absorbed by the world's seas. 

Scientists at Johns Hopkins University found the same winds that caused extremely low temperatures leading to higher levels of ozone depletion are speeding circulation patterns in polar waters, with the currents closer to the land pushing more deep water up to the ocean surface.

Scientists worry that the increasing upwelling of that water, hundreds of years old and naturally rich in carbon dioxide, is reducing the amount of manmade carbon absorbed by sub-polar waters.

Thanks to the Montreal Protocol we are well on our way to eliminating the use of CFCs. 

The damage however will go on for decades and more.

Further information on these most recent studies can be found in the Feb 15, 2013 issue of OzoNews click the following link here to download.

Hi "Critical Information"

"Tomorrow’s most-competitive products will rely heavily on what was learned from the life-cycles of today’s."

BY PETER A. BILELLO

Systems engineers and information-handling experts are joining forces to get a grip on the information explosion, thanks primarily to the timely convergence of systems engineering with digital design and development. And aptly so, since for two decades new product design has been a major pain point in information handling.

In the world of mechanical engineering, one of the biggest sources of the data explosion is new product development. Over the past two decades, the information flow has been transformed from a trickle of engineering drawings and scattered test data, all of it on paper, then to a few dozen 2-D CAD files, and today to a digital tsunami that touches every part of the organization.

These digital tools were originally intended for what might be called housekeeping in new product developmentgathering and organizing engineering data, and simplifying its retrieval. As the power of the tools was grasped, they were set to work doing things that had previously been impossible, or at least not cost-effective. These tasks included digital prototyping, cataloging legacy data, tracking customer-account information, storing know-how, and much more.

Information sources span the enterprise from concept development, through simulation and analysis, prototyping and finally to compliance with end-of-life disposal regulations. Users include purchasing, the enterprise resource planning system, finance, marketing, manufacturing engineering (ergonomics, quality assurance, and productivity), and field service, plus customers, suppliers, business partners, and distributors among many others.

These gushers of information reveal previously hidden small but profitable design opportunities, detect flaws earlier in the development process, recognize dead-ends sooner, winnow out many prototypes, and ultimately smooth out and accelerate manufacturing development. The downside of all this is equally clear: too much of a good thing.

The answer has been a powerful new shove for product lifecycle management, or PLM.

Every successful business strategy needs the coherence of a sound definition. CIMdata defines PLM as a strategic business approach that applies a consistent set of business solutions that support the collaborative creation, management, dissemination, and use of product definition information. PLM supports the extended enterprise (customers, designers, supply partners, etc.) from concept to the end of life of a product.

Beyond discrete manufacturing, where PLM started, it applies equally well in the process industries and in architectural-engineering-construction. In the process industries, PLM is focused on the plant itself (such as a refinery or a power-generating station); in AEC, it is focused on a building.

The rationale, PLM project managers say, is to ensure that the ideas and information driving the development of today’s products incorporate best practices and everything learned right up to the product-release date. Before a company can leverage its information, it must keep track of it. By integrating people, processes, business systems, and information, PLM can be the answer to that challenge.

A company’s new product digital data starts with conceptualizing. As the product idea takes form and enters development, the cascade swells with specifications, CAD models, results of tests and analyses, bills of materials, orders for tooling, and so on.

This information is reused, reformatted, and replicated in dozens of databases and decision points in purchasing, finance, marketing, manufacturing engineering, and field service. It’s also used to populate a manufacturer’s enterprise resource planning system and is depended upon by customers, suppliers, business partners, and distributors among others.

With all of these demands, sound management of information, and PLM in particular, pay off by finding needed information and avoiding its recreation whether as new CAD drawings or data re-entry.

PLM supports the extended enterprise (customers, designers, supply partners, etc.) from concept to the end of life of a product.

GLOBAL DRIVE

A big part of the information explosion stems from striving to come up with compelling new products amid global competition, which has driven the rapid expansion in the use of simulation and analysis software in the engineering industry.

Other big drivers are health, safety, and emissions regulations, and the fear of litigation.

“All the data will never exist in a single location,” said Christopher Hoffman, a systems engineering process leader at Cummins Inc., the diesel engine manufacturer in Columbus, Ind.

The PLM challenge at Cummins, he said, is that good product-development processes are available. “But individual engineers and technical people at the everyday working level frequently face fragmented and uncoordinated views of data and process support that he or she needs,” he said.

“The individual too often must manually re-enter data for different activities, and can only hope that the data properly aligns with data that others are using,” Hoffman said. “Such a work method is prone to process and data inaccuracies. Traceability is poor, and process efficiency suffers. It is a real challenge to effectively integrate process, data, work templates, and program management in a practical fashion.”

Systems engineering tools at Cummins provide accessible, convenient, and configurable work environments that appeal to both systems- and non-systems engineers, Hoffman said. The work of these engineers includes managing documents about departmental deliverables and evidence of delivery, requirements for traceability and critical parameters, failure mode effects analysis and risk management, systems validation and verification, and Six Sigma quality assurance.

All of these are components of PLM and it is the confluence of new product design and systems engineering that is driving the adoption of PLM. As with any new technology, good tools in the users’ hands support and eventually compel adoption.

IN THE KNOW

Dealing with tribal knowledge has been a significant issue for Bis-sell Homecare Inc., a 135-year-old floor-care appliances company in Grand Rapids, Mich. Tim Field, manager of mechanical design and CAD, and Alan Krebs, lead engineer for global technology and innovation, explained how Bissell uses knowledge-based engineering to extend the company’s tribal knowledge to its global business. This was triggered by Bissell’s rapid expansion overseas in recent years. Bissell has manufacturing operations in China, Korea, and Mexico, as well as the United States.

In manufacturing, tribal knowledge is unwritten but valuable information that accumulates and is shared within a work groupknow-how—but it is not often shared with others, at least not freely. The PLM challenge in dealing with tribal knowledge is that it lacks verification by analyses or other data, and is poorly linked to the enterprise’s information flows. Knowledge-based engineering, or KBE, ferrets out tribal knowledge with a combination of CAD, object-oriented programming, and artificial intelligence.

Krebs said that knowledge-based engineering “captures our global tribal knowledge with virtual models. This smart (and simple) geometry makes it easy to create ‘what-if’ designs that can be readily tested with simulation and analysis. A spreadsheet is used to drive the CAD geometry making it easy to use for all non-CAD users,” he added. What Bissell engineers get from this is “consistent and speedy creation of mechanical layouts, a push toward modularity, and the implementation of global design standards with tighter control, with more consistent design and engineering procedures.” This is yet another form of a single point of truth.

“Data reuse is also much greater,” Krebs noted, as opposed to recreating or redrawing with its penalties in time, cost, and design consistency. Along the way, Bissell engineers have firmly linked knowledge-based engineering with systems engineering.

“These gains allow performance breakthroughs to be readily shared across the global organization,” Krebs added. His background includes key roles in Bissell’s global technology and innovation unit and in new-business development.

Bissell’s knowledge-based engineering model for its upright vacuum cleaner includes over 300 direct and indirect performance characteristic values. These values control the specific geometries that contribute to best performance. In the past it would require weeks of effort to specify the desired values and build the 3-D mechanical layouts. With knowledge-based engineering, a 3-D mechanical layout can now be generated in less than a day.

SILOS OF EXPERTISE

According to Len Wozniak, process and tool systems architect for electronic controls and software at General Motors Co., a particular challenge is the tendency of different parts of a company to operate in silos. It is especially true of mechanical engineering and electrical engineering departments. As he laid it out, the problem has been the lack of a multidisciplinary orientation, tools, and capabilities in the development of the electronic controls in, for example, vehicle steering, braking, speed control, and similar systems.

Wozniak said his team has achieved some documented successes, which have big implications for PLM strategies. Among the challenges GM is overcoming are the lack of a multidisciplinary orientation, huge differences in the ways MEs and EEs report their design progress, and the metrics they use.

Engineering projects are managed with phase gates—points in development requiring a decision to proceed or not. In any project, decision points for MEs and EEs rarely coincide; this can greatly complicate the timing of management decisions.

The rapid increase in electronic controls and software that are being built into key auto components requires that MEs and EEs work ever more closely together. This highlights the need to integrate the very different approaches to development that the two disciplines use.

The internal engineering structures, or silos of expertise, add complications. So does the unfamiliarity of the typical auto industry ME with the ways in which software development is managed. In electronics, that process is product lifecycle engineering (PLE); roughly speaking PLE is PLM’s counterpart in electronics.

There was a time when many believed that, once everything went digital, everything would be simple for engineering departments.

Automotive product development traditionally focused on mechanical components. The main concerns were fit, function, and durability; until the advent of “mechatronics,” electronics and software were involved only peripherally.

Wozniak said two big areas where GM has had success have been in reducing engineering costs for electronic control units (ECUs) in brakes, steering, etc., and a significant reduction in warranty claims.

Engineering costs per ECU dropped by 26.5 percent the first time PLE approaches were married to customary ME methods. Engineering costs per ECU fell a further 9.75 percent the second time, he said.

The cost of warranty claims for all vehicles sold in the past seven years fell to 0.3 percent of vehicle cost from 1.07 percent.

Challenges remain in both tools and culture. Wozniak said tools are needed to manage parallel streams of development that occur when PLE and PLM are both in use. On the cultural side, he said, “While all product teams welcome the quality and cost benefits of PLE methods, few understand how they work.”

RETAINING INFORMATION

There was a time when many believed that, once everything went digital, everything would be simple for engineering departments. It turned out, of course, that going digital was anything but simple and straightforward. It made the world more complex and richer for it.

So much more could be done with computers and software than anyone had expected—simulation and analysis, for example, to slash the number of prototypes and compress manufacturing tryouts. One function of PLM is to make sure all the data in those analyses is retained, not just the conclusions.

Instead of fading away, specialties and divisions of expertise multiplied. Looking past the very real technical challenges of data connectivity and interoperability, from the PLM and information-handling standpoint, silos are a big systems-engineering issue because their organizational charts are dynamic and their workflows ever-changing.

Today, companies are striving to enhance the value of the information they hold, to prevent its loss, and to find innovative ways to use it. The challenge is that critical information originates in many different departments, locations, and formats.

How does a company keep its engineers from redrawing the wheel? How does it take a good practice from a plant in the American Midwest and make it available to branches around the world? Many companies say they are turning to PLM systems to do it.

Peter A. Bilello is the president of CIMdata Inc., a consultancy in product life-cycle management in Ann Arbor, Mich.