A review of regional and global estimates of unconventional gas resources

This Research Report assesses the currently available evidence on the size of unconventional gas resources at the regional and global level. Focusing in particular on shale gas, it provides a comprehensive summary and comparison of the estimates that have been produced to date. It also examines the methods by which these resource estimates have been produced the strengths and weaknesses of those methods, the range of uncertainty in the results and the factors that are relevant to their interpretation

An Overview of the NASA Advanced Composites Consortium High Energy Dynamic Impact Phase II Technical Path

Advanced composite structures are increasingly becoming the norm for use in military and commercial aircraft. Many of these structures are in places that are prone to high energy dynamic impact (HEDI) such as a wing or fuselage structures subjected to bird strike or a fan blade out event. Certification testing is expensive and industry currently lacks to the tools to perform reliable certification by analysis or smarter testing. As such, the NASA Advanced Composites Consortium HEDI team was formed with representatives from aerospace original equipment manufacturers, government research laboratories, and academia to advance the state-of-the-art in emerging progressive damage and failure analysis (PDFA) methods in a two phase program. These PDFA approaches have the ability to predict ply-by-ply level damage in composite structures, but to date, have not been thoroughly vetted for HEDI events. In this paper, the technical path that is used in Phase II of the program is presented

Maximizing Intellectual Property and Intangible Assets: Case Studies in Intangible Asset Finance

As innovative companies struggle to raise funds, intellectual property and intangible assets are providing alternative ways of financing innovation. But greater awareness of them as an asset class is needed. Raising that awareness is the focus of a new report from Athena Alliance, Maximizing Intellectual Property and Intangible Assets: Case Studies in Intangible Asset Finance by Ian Ellis, a former U.S. Department of Commerce official specializing in intellectual property and international trade. The report outlines increasing, but still nascent, means of financing innovation based on these assets in public, private and venture capital markets. As industry has invested capital in research and development to develop new technology and advance other creative activities, intellectual capital has become a valuable asset class, according to the paper. In response, firms specializing in intangible-based financing are springing up, using them to raise capital for the next round of innovation.The paper details equity, equity-debt, debt, and sale-leaseback transactions, both private and public, that have helped companies raise capital, based on careful, rigorous analysis and conservative underwriting standards. For example, the author notes that in 2000, there were two public deals using royalty securitization, raising $145 million. In 2007-08,$3.3 billion was raised in 19 deals.Unlike some of the exotic financial vehicles, however, the financial products discussed in this paper are some of the most basic financing mechanisms in business. The innovation is in recognizing the value of intangible assets for corporate finance. These new financial firms are using traditional financial techniques in new ways to help innovative companies.But more should be done.One important step would be developing sound, industry-wide, underwriting standards, according to the report. For example, Small Business Administration (SBA) rules permit its loans to be used for acquisition of intangible assets when buying on-going businesses. Rules are unclear on whether those assets can be used as collateral. The paper recommends that SBA work with commercial lenders to develop standards for using intangible assets as collateral.The report builds on earlier Athena Alliance papers, notably Intangible Asset Monetization: The Promise and the Reality

ME Design and Freeform Fabrication of Compliant Cellular Materials with Graded Stiffness

Typically, cellular materials are designed for structural applications to provide stiffness or absorb impact via permanent plastic deformation. Alternatively, it is possible to design compliant cellular materials that absorb energy via recoverable elastic deformation, allowing the material to spring back to its original configuration after the load is released. Potential applications include automotive panels or prosthetic applications that require repeated, low-speed impact absorption without permanent deformation. The key is to arrange solid base material in cellular topologies that permit high levels of elastic deformation. To prevent plastic deformation, the topologies are designed for contact between cell walls at predetermined load levels, resulting in customized, graded stiffness profiles. Design techniques are established for synthesizing cellular topologies with customized compliance for static or quasi-static applications. The design techniques account for cell wall contact, large scale deformations, and material nonlinearities. Resulting cellular material designs are fabricated with selective laser sintering, and their properties are experimentally evaluated.Mechanical Engineerin

The Limitations of Optimization from Samples

In this paper we consider the following question: can we optimize objective functions from the training data we use to learn them? We formalize this question through a novel framework we call optimization from samples (OPS). In OPS, we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint. While there are interesting classes of functions that can be optimized from samples, our main result is an impossibility. We show that there are classes of functions which are statistically learnable and optimizable, but for which no reasonable approximation for optimization from samples is achievable. In particular, our main result shows that there is no constant factor approximation for maximizing coverage functions under a cardinality constraint using polynomially-many samples drawn from any distribution. We also show tight approximation guarantees for maximization under a cardinality constraint of several interesting classes of functions including unit-demand, additive, and general monotone submodular functions, as well as a constant factor approximation for monotone submodular functions with bounded curvature

Unconventional gas: potential energy market impacts in the European Union

In the interest of effective policymaking, this report seeks to clarify certain controversies and identify key gaps in the evidence-base relating to unconventional gas. The scope of this report is restricted to the economic impact of unconventional gas on energy markets. As such, it principally addresses such issues as the energy mix, energy prices, supplies, consumption, and trade flows. Whilst this study touches on coal bed methane and tight gas, its predominant focus is on shale gas, which the evidence at this time suggests will be the form of unconventional gas with the most growth potential in the short- to medium-term. This report considers the prospects for the indigenous production of shale gas within the EU-27 Member States. It evaluates the available evidence on resource size, extractive technology, resource access and market access. This report also considers the implications for the EU of large-scale unconventional gas production in other parts of the world. This acknowledges the fact that many changes in the dynamics of energy supply can only be understood in the broader global context. It also acknowledges that the EU is a major importer of energy, and that it is therefore heavily affected by developments in global energy markets that are largely out of its control.JRC.F.3-Energy securit

Independent Expert Scientific Panel – Report on Unconventional Oil and Gas

No abstract available

Searching for Optimal Runtime Assurance via Reachability and Reinforcement Learning

A runtime assurance system (RTA) for a given plant enables the exercise of an untrusted or experimental controller while assuring safety with a backup (or safety) controller. The relevant computational design problem is to create a logic that assures safety by switching to the safety controller as needed, while maximizing some performance criteria, such as the utilization of the untrusted controller. Existing RTA design strategies are well-known to be overly conservative and, in principle, can lead to safety violations. In this paper, we formulate the optimal RTA design problem and present a new approach for solving it. Our approach relies on reward shaping and reinforcement learning. It can guarantee safety and leverage machine learning technologies for scalability. We have implemented this algorithm and present experimental results comparing our approach with state-of-the-art reachability and simulation-based RTA approaches in a number of scenarios using aircraft models in 3D space with complex safety requirements. Our approach can guarantee safety while increasing utilization of the experimental controller over existing approaches