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At least 37 records · Page 2

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DOE OSTI2022

Feasibility of critical infrastructure protection using network functions for programmable and decoupled ICS policy enforcement over WAN

Industrial control systems (ICS) represent a major component of our critical infrastructure. With the increasing need for more control and monitoring of such systems, ICS have seen an increase in connectivity to wide area networks (WAN) exposing aging equipment to rapidly evolving cybersecurity threats. Furthermore, the ICS data requires a reliability measure from the networks for critical functions for infrastructure monitoring and control. Especially when remote plant sites are involved such as pipelines, energy distribution networks, and transportation, WAN transport impairments most often provide a best effort delivery with no strict reliability guarantees. Network functions can provide a vendor agnostic, programmable critical infrastructure protection with a single maintenance, policy determination, and reliability assurance surface. A network function (NF) can be utilized for policy enforcement over the communication between remote entities and the main control office. This paper presents the research on transparent integration with existing ICS without disrupting communications, resulting in minimal downtime while decoupling the fast paced evolution of defensive security measures from the upgrade cycle of expensive long term hardware. We report our measurements on the resource requirements and overhead in the network for successful NF insertion under a wide variety of network impairments (network packet delay, reordering, and loss). Our paired NF implementation provides a policy enforcement platform extensible to cover myriad cybersecurity-related communication goals, including packet signing for verification, encryption for data privacy, packet filtering and data diode operation (i.e. protecting against eavesdropping, packet injection, and denial-of-service). Furthermore, bundling communication specifications into packet flows allows for tunability in applying policies as coarse- or fine-grained as the needs of the operator. We report on network function resource requirements in the form of required queue depth and network utilization overhead to inform the decision making against hardware cost constraints.

42 ENGINEERING↗

DOE OSTI2015

Obligation of occupational safety and health (OSH) legislation by designers and manufacturers: Perception of enforcement officers

Designers and manufacturers of plants are responsible to design or redesign the process, product and workplace with consideration of eliminating hazards or controlling risks as early as possible at design stage.The purpose of this paper is to determine the perception of enforcement officers towards compliance and implementation of OSH legislation by the designers and manufacturers of plant.The research partners was a goverment department that enforce the related OSH laws to designers and manufacturers of the plant. A total of 59 technical staffs were surveyed together with examination of the sekunder data from the department to evaluate overall OSH legal obligation by the industries. This study demonstrate how OSH regulators play the roles to influence the industries to perform better in OSH.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

LiveWall Operational Evaluation: Seattle Law Enforcement Pilot

The LiveWall concept envisioned as an outgrowth of the Precision Information Environment (PIE) project allows communications between separate groups using interactive video, audio, and a shared desktop environment; this allows everyone to participate and collaborate in real time, regardless of location. The LiveWall concept provides a virtual window to other locations, where all parties can interact and collaboratively work with each other. This functionality is intended to improve multi-site coordination amongst emergency operations centers (EOC), field operations sites and across organizations and jurisdictions to accommodate communications during routine and emergency events. For the initial LiveWall operational evaluation PNNL partnered with the Seattle Police Department (SPD). This partnership allowed for the creation of an excellent LiveWall test bed specific to law enforcement. This partnership made it possible to test the LiveWall concept with scenarios involving the many facets of the law enforcement work done by SPD. PNNL and SPD agreed that integrating the systems into operations for a real event would be the best test of the technology and give SPD staff greater visibility into the functionality and benefits offered by the LiveWall concept.

Communications↗

DOE OSTI2013

Utilization of Local Law Enforcement Aerial Resources in Consequence Management (CM) Response

During the past decade the U.S. Department of Homeland Security (DHS) was instrumental in enhancing the nation’s ability to detect and prevent a radiological or nuclear attack in the highest risk cities. Under the DHS Securing the Cities initiative, nearly 13,000 personnel in the New York City region have been trained in preventive radiological and nuclear detection operations, and nearly 8,500 pieces of radiological detection equipment have been funded. As part of the preventive radiological/nuclear detection (PRND) mission, several cities have received funding to purchase commercial aerial radiation detection systems. In 2008, the U.S. Department of Energy, National Nuclear Security Administration Aerial Measuring System (AMS) program started providing Mobile Aerial Radiological Surveillance (MARS) training to such assets, resulting in over 150 HAZMAT teams’ officers and pilots from 10 law enforcement organizations and fire departments being trained in the aerial radiation detection. From the beginning, the MARS training course covered both the PRND and consequence management (CM) missions. Even if the law enforcement main focus is PRND, their aerial assets can be utilized in the collection of initial radiation data for post-event radiological CM response. Based on over 50 years of AMS operational experience and information collected during MARS training, this presentation will focus on the concepts of CM response using aerial assets as well as utilizing law enforcement/fire department aerial assets in CM. Also discussed will be the need for establishing closer relationships between local jurisdictions’ aerial radiation detection capabilities and state and local radiation control program directors, radiological health department managers, etc. During radiological events these individuals may become primary experts/advisers to Incident Commanders for radiological emergency response, especially in the early stages of a response. The knowledge of the existence, specific capabilities, and use of local aerial radiation detection systems would be critical in planning the response, even before federal assets arrive on the scene. The relationship between local and federal aerial assets and the potential role for the further use of the MARS training and expanded AMS Reachback capabilities in facilitating such interactions will be discussed.

consequence management, CM, local law enforcement,↗

DOE OSTI2023

Millimeter-Thick Liquid Crystalline Elastomer Actuators Prepared by-Surface-Enforced Alignment

Here, liquid crystalline elastomers (LCE) are thermally cyclable, compliant actuators with compelling mechanical properties. The large and programmable deformation of LCE has led to numerous functional examinations spanning optics, medical devices, and robotics. A well-established method to prepare complex LCE actuators is to utilize surface-enforced photoalignment. Herein, a facile and scalable approach is reported to circumvent the physical limits of surface-enforced alignment (e.g., samples that are 50 µm or less) to amplify the achievable force output in LCE. Applying an approach termed direct layering, the thermomechanical response of LCE elements prepared with +1 disclination patterns in a range of compositions and thicknesses is contrasted. The design and preparation of +1 disclination patterns and arrays is explored to assess the contribution of sample geometry and overlap to deformation and force output. The methodology detailed in this contribution allows for the preparation of elements ≈1 mm in thickness that are capable of actuating large objects. Furthermore, the fabrication of these elements uniquely enables the realization of mechanical instabilities to hasten the actuation rate in response to thermal change and to enable leaping.

36 MATERIALS SCIENCE↗

DOE OSTI2020

An explicit scheme to enforce charge conservation in transient Particle-in-Cell simulations with Maxwell-Boltzmann electrons

The Maxwell-Boltzmann electron model is a very popular approximation of the electronic behavior in electrostatic plasmas, adopted in all cases where the electron dynamics is governed only by a balance between electric and pressure forces. In such a model, the electron dynamics is reduced to solving for a nonlinear Poisson problem together with an additional expression enforcing charge conservation. In this work we derive an expression of charge conservation which can be conveniently applied to explicit schemes to update the reference Boltzmann density, particularly useful when simulating plasma sheaths. The scheme can be equally applied to both steady-state and transient problems. Two examples are shown, a steady-state plasma sheath, and a radio-frequency magnetic presheath. Our proposed scheme allows to enforce global charge conservation locally in time, and can thus be applied to the simulation of transient phenomena.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Numerical investigation of magneto-inertial fusion targets magnetized by dynamic enforcement of helical current flow

Magnetized Liner Inertial Fusion (MagLIF) targets require premagnetization to reduce thermal conduction losses from the laser-heated fuel to the liner material during implosion. MagLIF targets are typically magnetized by external magnetic field coils which are technologically limited to providing <20 T axial magnetic fields in the fusion fuel. We present a novel target design, AutoMag-D, which employs dynamic enforcement of helical current in the liner, resulting in >20 T axial magnetic field in the fuel region prior to implosion without the need of external magnetic field coils. These self-magnetizing liners are made of helically oriented alternating electrically conductive material and electrically insulating material surrounded by a thin, conductive outer radial layer. As the liner is pulsed with a ∼ 20 MA, ∼ 100 ns rise time drive current from Z, the outer layer of conductive material is shocked and intensely Joule heated, causing it to melt, vaporize, and turn to plasma. This allows the magnetic drive field to diffuse radially inward which dynamically enforces current flow in the helical conduction paths in the liner and produces axial magnetic field in the fuel region prior to implosion of the inner liner surface. AutoMag-D liner designs do not require dielectric breakdown of electrically insulating material (as in traditional auto-magnetizing helical liners) and do not require helical return current geometries (as in dynamic screw pinches). We present results from three-dimensional radiation-magnetohydrodynamic simulations of MagLIF implosions employing AutoMag-D liner designs. Simulated AutoMag-D targets demonstrate improved fuel conditions and thermonuclear yield compared to traditional MagLIF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

X Fe 4 Ge 2 ( X = Y , Lu ) and Mn 3 Pt : Filling-enforced magnetic topological metals

Magnetism, coupled with nontrivial band topology, can bring about many interesting and exotic phenomena, so that magnetic topological materials have attracted persistent research interest. However, compared with nonmagnetic topological materials (TMs), the magnetic TMs are less studied, since their magnetic structures and topological phase transitions are usually complex and the first-principles predictions are usually sensitive on the effect of Coulomb interaction. Here we present a comprehensive investigation of XFe 4 Ge 2 (X = Y, Lu) and Mn 3 Pt, and find these materials to be filling-enforced magnetic topological metals. Our first-principles calculations show that XFe 4 Ge 2 (X = Y, Lu) host Dirac points near the Fermi level at high symmetry point S. These Dirac points are protected by $\text{P}\mathcal{T}$ symmetry ($\text{P}$ and $\mathcal{T}$ are inversion and time-reversal transformations, respectively) and a twofold screw rotation symmetry. Moreover, through breaking $\text{P}\mathcal{T}$ symmetry, the Dirac points would split into Weyl nodes. Mn 3 Pt is found to host fourfold degenerate band crossings in the whole high symmetry path of A – Z . We also utilize the GGA + U scheme to take into account the effect of Coulomb repulsion and find that the filling-enforced topological properties are naturally insensitive on U .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Iterative Stability Enforcement in Adaptive Antoulas–Anderson Algorithms for ${\boldsymbol{\mathcal{H}_2}}$ Model Reduction

This paper presents an extension of the Adaptive-Antoulas-Anderson (AAA) algorithm for rational modelling. Specifically, our new stable multi-input multi-output AAA (smiAAA) algorithm builds rational approximations of multi-input signals with a common set of stable poles. A new methodology is presented for iteratively enforcing stability constraints on the poles. We demonstrate the strengths of this approach compared to the stability enforcement in the FastAAA algorithm. Results using the smiAAA algorithm are compared with the commonly used Vector Fitting algorithm and the more recently published RKFIT algorithm. Vector Fitting and RKFIT both require the user to input the number of poles to use in the approximations. If the final approximation is not accurate enough, the user must re-start Vector Fitting or RKFIT with a larger number of poles and/or a new starting location for the poles. In contrast, the smiAAA algorithm is designed to allow the user to simply input the desired accuracy of the approximations, and the necessary number of poles is detected automatically. This permits users to produce approximations of a desired accuracy with no knowledge about the underlying order of the system being approximated, preventing the algorithm from ever needing to be rerun. An additional feature for preventing extraneous poles from being returned by AAA is also discussed. The cause of these extraneous poles is efficiently detected and removed by our presented methodology. In conclusion, the examples presented demonstrate that smiAAA can efficiently produce approximations of similar or better accuracy than Vector Fitting and RKFIT while requiring less input from the user.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

The Cost of Enforcing Building Energy Codes: Phase 2

The purpose of this study is to present key findings regarding costs associated with enforcing building energy code compliance–primarily focusing on costs borne by local government. Building codes, if complied with, have the ability to save a significant amount of energy. However, energy code compliance rates have been significantly lower than 100%. Renewed interest in building energy codes has focused efforts on increasing compliance, particularly as a result of the 2009 American Recovery and Reinvestment Act (ARRA) requirement that in order for states to receive additional energy grants, they must have “a plan for the jurisdiction achieving compliance with the building energy code…in at least 90 percent of new and renovated residential and commercial building space” by 2017 (Public Law 111-5, Section 410(2)(C)). One study by the Institute for Market Transformation (IMT) estimated the costs associated with reaching 90% compliance to be $810 million, or $610 million in additional funding over existing expenditures, a non-trivial value. [Majersik & Stellberg 2010] In this context, Lawrence Berkeley National Laboratory (LBNL) conducted a study to better pinpoint the costs of enforcement through a two-phase process.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Enhanced Safety of Heavy-Duty Vehicles on Highways through Automatic Speed Enforcement – A Simulation Study

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. Here, this paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios. To conduct this analysis, we utilize SUMO, an open-source microscopic traffic simulator, enabling realistic simulations of real-world driving behaviors with and without speed cameras. The simulations also consider empirical distributions of desired headway gaps and reaction times exhibited by human drivers. This study offers a fresh perspective on highway safety by examining how the influence of automatic speed enforcement systems on driver behavior enhances the safety of HDV when faced with challenging situations. By shedding light on these dynamics, we contribute valuable insights to the ongoing research efforts on using technology to create safer roadways, ultimately promoting safer coexistence between heavy-duty and passenger vehicles.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning

We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability. In particular, we provide ways to enforce constraints during training for all three major modes of learning, namely supervised, unsupervised and reinforcement learning. In general, the dynamic constraints need to include terms which are analogous to memory terms in model reduction formalisms. Such memory terms act as a restoring force which corrects the errors committed by the learned flow map during prediction. For supervised learning, the constraints are added to the objective function. For the case of unsupervised learning, in particular generative adversarial networks, the constraints are introduced by augmenting the input of the discriminator. Finally, for the case of reinforcement learning and in particular actor-critic methods, the constraints are added to the reward function. In addition, for the reinforcement learning case, we present a novel approach based on homotopy of the action-value function in order to stabilize and accelerate training. We use numerical results for the Lorenz system to illustrate the various constructions.

Stinis, Panagiotis↗

DOE OSTI2012

ACE Learning Series - Enforcement Toolkit

The Building Energy Codes Program (BECP) developed this toolkit to help the enforcement community achieve higher levels of compliance with building energy codes. Although this toolkit is not intended for use by designers, developers, homeowners or renters, building owners, or building operators, it may provide those individuals some insight into the issues facing the enforcement community.

IECC, building information modeling, HVAC, new con↗

DOE OSTI2019

ESPC ESA Case Study: Energy Affordability at the Drug Enforcement Administration

The U.S. Drug Enforcement Administration (DEA) El Paso Intelligence Center (EPIC), located on Fort Bliss in El Paso, TX, installed 2.5 MW DC of photovoltaics (PV). This project was an energy sales agreement energy conservation measure (ESA ECM) using the U.S. Department of Energy (DOE) energy savings performance contract (ESPC) ENABLE contract vehicle. The project represents an economical clean-energy solution that provides excellent construction and other trades-related jobs, and provides information that will help future ESPC ESA projects provide cost savings to the government.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Where There’s a Way, There's a Will: The World Customs Organization's Capacity-Building in the Field of Strategic Trade Control Enforcement

The World Customs Organization’s (WCO) approach to capacity building in the domain of strategic trade control enforcement has been exceptionally effective. This article outlines what the WCO has done, documents some of the impacts, and examines factors contributing to its effectiveness. An important factor contributing to the WCO's success has been a focus on influencing senior policy makers at the national level in addition to upgrading working-level skills and abilities. This strategy in turn owes its success, at least in part, to the WCO’s legitimacy and norm-setting ability as a member-driven international organization, a customizable approach adaptive to various national circumstances, adherence to universal international mandates such as United Nations Security Council Regulation 1540, and the ability to couple training and operational activities.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2016

Efficient production of platelets from mouse embryonic stem cells by enforced expression of Gata2 in late hemogenic endothelial cells

Platelets are essential for blood circulation and coagulation. Previous study indicated that overexpression of Gata2 in differentiated mouse embryonic stem cells (ESCs) resulted in robust induction of megakaryocytes (Mks). To evaluate platelet production capacity of the Gata2-induced ESC-derived Mks, we generated iGata2-ESC line carrying the doxycycline-inducible Gata2 expression cassette. When doxycycline was added to day 5 hemogenic endothelial cells in the in vitro differentiation culture of iGata2-ESCs, c-Kit{sup −}Tie2{sup −}CD41{sup +} Mks were predominantly generated. These iGata2-ESC-derived Mks efficiently produced CD41{sup +}CD42b{sup +}CD61{sup +} platelets and adhered to fibrinogen-coated glass coverslips in response to thrombin stimulation. Transmission electron microscopy analysis demonstrated that the iGata2-ESC-derived platelets were discoid-shaped with α-granules and an open canalicular system, but were larger than peripheral blood platelets in size. These results demonstrated that an enforced expression of Gata2 in late HECs of differentiated ESCs efficiently promotes megakaryopoiesis followed by platelet production. This study provides valuable information for ex vivo platelet production from human pluripotent stem cells in future. -- Highlights: •Megakaryocytes are efficiently induced by Gata2 from ESC-derived day 5 HECs. •Gata2-induced ESC-derived megakaryocytes are c-Kit{sup −}Tie2{sup −}CD41{sup +}. •Gata2-induced ESC-derived megakaryocytes produce larger discoid-shaped platelets. •Gata2-induced ESC-derived platelets bind fibrinogen upon thrombin stimulation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Exact enforcement of temporal continuity in sequential physics-informed neural networks

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.

42 ENGINEERING↗