Open RAN Security -Current Progress and Next Steps
5G Open RAN security talk given to the IEEE Open RAN Security Working Group.
5G↗
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Results for “Open RAN”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.
5G↗
Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds
Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.
5G↗
Machine Learning–Guided Boolean Matrix Inference for Real-Time O-RAN Conflict Detection
Open Radio Access Networks (O-RAN) are emerging, software-driven cellular architectures that promote flexibility by enabling components from different vendors to interoperate. Multiple control applications called xApps can independently adjust network parameters in near real time, often without awareness of each other's actions. This creates a system highly prone to unintended conflicts and performance degradation due to the inherent complexity of such openness. To model such systems and ultimately prevent or mitigate xApp conflicts, it is essential to understand the dynamic relationships between xApps (A), the control parameters they adjust (P), and the resulting KPI responses (K). While the mappings from A to P and from K to A can often be derived from xApp specifications, the relationship from P to K is typically hidden within the system’s dynamics and must be inferred from observed data. We propose a novel data-driven Boolean inference framework that uncovers the hidden P?K dependencies using machine learning and interpretable rule induction. Continuous parameters and KPIs are first binarized using decision tree classifiers, and a binary influence matrix L is then inferred by solving Boolean matrix equations over time. This compact representation improves interpretability and enables real-time tracking of dynamically evolving parameter-KPI dependencies. We demonstrate the effectiveness of our method in a realistic mobile handover scenario, where it accurately recovers the underlying logic and enables proactive conflict detection.
42 - ENGINEERING↗
Risk-Aware Reinforcement Learning Framework for User-Centric O-RAN
The evolution of Open Radio Access Networks (O-RAN) presents an opportunity to enhance network performance by enabling dynamic orchestration of configuration and optimization parameters (COPs) through online learning methods. However, leveraging this potential requires overcoming the limitations of traditional cell-centric RAN architectures, which lack the necessary flexibility. On the other hand, despite their recent popularity, the practical deployment of online learning frameworks, such as Deep Reinforcement Learning (DRL)-based COP optimization solutions, remains limited due to their risk of deteriorating network performance during the exploration phase. In this article, we propose and analyze a novel risk-aware DRL framework for user-centric RAN (UC-RAN), which offers both the architectural flexibility and COP optimization to exploit this flexibility. We investigate and identify UC-RAN COPs that can be optimized via a soft actor-critic algorithm implementable as an O-RAN application (rApp) to jointly maximize latency satisfaction, reliability satisfaction, area spectral efficiency, and energy efficiency. We use the offline learning on UC-RAN to reliably accelerate DRL training, thus minimizing the risk of DRL deteriorating cellular network performance. Results show that our proposed solution approaches near-optimal performance in just a few hundred iterations with a decrease in risk score by a factor of ten.
6G and beyond↗
Final Scientific/Technical Report: A Hierarchical, Secure, Wireless Communications Network Architecture for the Smart Electric Grid, Applicable to Digital Twin
Explore the source record for details and available documents.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Unsteady RANS and Scale Resolving Simulations of Open Rotor Noise
Computational simulations using structured curvilinear overset grids with the Launch Ascent and Vehicle Aerodynamics (LAVA) framework are presented for predicting the aerodynamic and aeroacoustic behavior of a contra-rotating open rotor (CROR) propulsion system using a newly developed and computationally efficient sliding mesh technique. A comprehensive evaluation of two turbulence closure treatments is carried out as part of the scope of this work: Unsteady Reynolds-Averaged Navier-Stokes Simulation (URANS), and Hybrid RANS/LES (HRLES). The computed results are then validated against existing experimental measurements at both the nominal take-off and cruise flight conditions. To propagate the noise generated by the near-field CROR system, aeroacoustic analysis based on a frequency-domain permeable surface Ffowcs Williams-Hawkings (FW-H) approach is applied.
ARMD↗
Unsteady RANS and Scale Resolving Simulations of Open Rotor Noise
Computational simulations using structured curvilinear overset grids with the Launch Ascent and Vehicle Aerodynamics (LAVA) framework are presented for predicting the aerodynamic and aeroacoustic behavior of a contra-rotating open rotor (CROR) propulsion system using a newly developed and computationally efficient sliding mesh technique. A comprehensive evaluation of two turbulence closure treatments is carried out as part of the scope of this work: Unsteady Reynolds-Averaged Navier-Stokes Simulation (URANS), and Hybrid RANS/LES (HRLES). The computed results are then validated against existing experimental measurements at both the nominal take-off and cruise flight conditions. To propagate the noise generated by the near-field CROR system, aeroacoustic analysis based on a frequency-domain permeable surface Ffowcs Williams-Hawkings (FW-H) approach is applied.
ARMD↗
Conflict Detection in Open Radio Access Network (O-RAN) Control
A brief overview of the O-RAN approach to 5G cellular networking, discussion of the problem of conflicts among control functions under this paradigm, and research toward an approach detecting these conflicts using machine learning. This talk provides a high-level overview of academic research associated with an ongoing LDRD.
5G↗
Slow Solar Wind Connection Science during Solar Orbiter's First Close Perihelion Passage
The Slow Solar Wind Connection Solar Orbiter Observing Plan (Slow Wind SOOP) was developed to utilize the extensive suite of remote-sensing and in situ instruments on board the ESA/NASA Solar Orbiter mission to answer significant outstanding questions regarding the origin and formation of the slow solar wind. The Slow Wind SOOP was designed to link remote-sensing and in situ measurements of slow wind originating at open–closed magnetic field boundaries. The SOOP ran just prior to Solar Orbiter's first close perihelion passage during two remote-sensing windows (RSW1 and RSW2) between 2022 March 3–6 and 2022 March 17–22, while Solar Orbiter was at respective heliocentric distances of 0.55–0.51 and 0.38–0.34 au from the Sun. Coordinated observation campaigns were also conducted by Hinode and IRIS. The magnetic connectivity tool was used, along with low-latency in situ data and full-disk remote-sensing observations, to guide the target pointing of Solar Orbiter. Solar Orbiter targeted an active region complex during RSW1, the boundary of a coronal hole, and the periphery of a decayed active region during RSW2. Postobservation analysis using the magnetic connectivity tool, along with in situ measurements from MAG and SWA/PAS, showed that slow solar wind originating from two out of three of the target regions arrived at the spacecraft with velocities between ∼210 and 600 km s −1 . The Slow Wind SOOP, despite presenting many challenges, was very successful, providing a blueprint for planning future observation campaigns that rely on the magnetic connectivity of Solar Orbiter.
Slow solar wind↗
MINERvA open-data product
MINERvA is THE neutrino cross section experiment Scintillator tracker/calorimeter ran in the NuMI beam at Fermilab same beam as the MINOS and NOvA oscillation experiments With our data, we are solving systematic shortcomings in neutrino interaction rate/spectra that are the largest part of the systematic uncertainty in today s (and tomorrow s) measurements. Some aspects have NO equivalent in the neutrino program future. Scientific scope includes both particle and nuclear physics GeV scale cross sections, A dependence, MeV scale effects most published measurements for a neutrino experiments ever (well, tied with T2K) with 25% more papers in the pipeline.
Gran, Rik [Minnesota U., Duluth] (ORCID:0000000216↗
Comparison of URANS and LES predictions for the open phase of the OECD NEA CSNI fluid structure interaction CFD benchmark
The OECD NEA CSNI WGAMA CFD Task Group ran a benchmark in 2020 and 2021 to assess the predictive capabilities of coupled fluid structure interaction (FSI) CFD analysis methods. This paper presents the predictions made for the open phase of the benchmark using URANS and LES turbulence modelling approaches, and a comparison of the results to the experimental data. The benchmark comprised a channel containing two inline cylinders in cross-flow. The cylinders were fixed at one end, free at the other, and had measured resonant frequencies and damping properties. The URANS modelling used ANSYS Fluent 2-way coupled to ANSYS Mechanical. The LES modelling used Nek5000, 1-way coupled to Diablo. Comparisons with cross-channel velocity profiles are presented, both for the mean flow and its RMS. Comparisons are also made to the frequency spectra for point measurements of fluid velocity and pressure, and for the accelerations of the free end of each cylinder. URANS predicts the average velocity profiles relatively well, and is able to predict the velocity and acceleration spectra at the shedding frequency. However, the frequency content at the 4th harmonic of the shedding frequency is low in the URANS flow fields, and so does not excite accelerations at the resonant frequency of the cylinders. LES makes better predictions of the average profiles, and the velocity spectra agree well at both the shedding frequency and at higher frequencies. In conclusion, the 1-way coupled LES results show good agreement for acceleration spectra.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Chemically Recyclable Analogs of Styrene–Butadiene Copolymers Enabling Perfectly Linear Ethylene–Styrene Materials with Random Phenyl Distribution
Copolymerization of cyclopentene (CP) and 4-phenylcyclopentene (4PCP) at a full range of comonomer feed ratios is reported using Ru-based ring-opening metathesis polymerization (ROMP) yielding homogeneous copolymers analogous to poly(styrene-ran-1,4-butadiene) and poly(ethylene-ran-styrene) copolymers following hydrogenation under mild conditions. In all cases, total monomer conversions of 86%–92% yielded copolymers with compositions within 4% of monomer feeds. Analysis of equilibrium copolymerization thermodynamics, rarely performed on two cycloolefin monomers with low ring strain energies, provides rational design strategies for negotiating two monomers with different equilibrium monomer concentrations. Inverse-gated decoupled 13 C NMR analysis of dyad sequences on the resulting copolymer microstructures concludes a near-random distribution of comonomer units. The copolymers produced from ROMP have number-average molar masses up to 60 kg mol –1 , moderate dispersities (1.5 ± 0.1), and high trans olefin content (86% ± 2%) while glass-transitions temperatures follow the Fox equation and span the full range between homopolymer extremities of PCP (−96 °C) and P4PCP (17 °C). Unlike most prevulcanized elastomers, these materials undergo facile chemical recycling to monomer, producing complete ring-closing metathesis depolymerization (RCMD) of the polymer back to the CP comonomers. Quantitative olefin hydrogenation produced perfectly linear polyethylene with 4%–16% of the backbone carbons containing a phenyl pendant, analogous to ES copolymers with up to 71.5% w/w styrene units but with random distribution of the aromatic pendants. Thermal properties of these materials are discussed, which span from semicrystalline to amorphous, and with T g values notably less than the reported ES copolymer analogs at similar compositions.
animal feed↗
Science Uses Deployment Operations-Advanced Wireless: Exploring Open Radio Access Network Technologies for Energy Science
Open Radio Access Network is emerging as a solution to the increasing demand for more flexible, cost-effective, and advanced mobile network infrastructures. This evolution is driven by advancements in wireless technologies and the growing complexity of deploying and managing these networks. O-RAN represents a significant shift in wireless technology, building upon the 3rd Generation Partnership Project framework to foster openness, flexibility, and interoperability. By decoupling hardware and software components, Open Radio Access Network enables a multi-vendor ecosystem that encourages innovation and diverse solutions. Open Radio Access Network's potential extends beyond traditional wireless applications, with growing interest in its role in advancing energy systems, particularly in the context of smart grids, microgrids, and the integration of renewable energy sources. While the role of open-wireless technologies in driving energy transformation is increasingly recognized, further exploration is needed. Vendors and utilities are investigating how Open Radio Access Network technologies can optimize energy use cases and improve the performance of 5G and beyond applications. This report outlines efforts under the Science Uses Deployment Operations Advance Wireless project, a collaboration between the National Laboratory of the Rockies' Cybersecurity Research Center, Argonne National Laboratory, Lawrence Berkeley National Laboratory, and the Department of Energy's Energy Science Network research and operations staff. The focus of this project is on due diligence, through testing and evaluation, preparing for the deployment of advanced wireless infrastructure for scientific use cases, with an emphasis on Open Radio Access Network technology, its components, integrations, and its ability to support vertical stack application across the energy sector. Additionally, the report highlights the value cases for utilities, underscoring how adopting open wireless standards can accelerate the evolution of energy systems, foster innovation, and improve the integration of critical energy technologies.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Multidimensional Modeling of Mixture Formation in a Hydrogen-Fueled Heavy-Duty Optical Engine With Direct Injection
Hydrogen (H 2 ), as a carbon-free fuel, is considered as one of the most promising solutions to reduce the carbon footprint of hard-to-decarbonize energy and transportation sectors. As such, hydrogen-fueled internal combustion engines (H 2 ICEs) have recently been receiving increasing attention, particularly in applications such as on-road/off-road heavy-duty transport and combined heat and power. The direct injection (DI) of gaseous hydrogen into the combustion chamber offers great potential for achieving high power density and high engine efficiency, while mitigating the risk of backfire and reducing pre-ignition. However, the numerical simulation of H 2 DI system remains a formidable challenge associated with the high computational cost of reproducing compressible supersonic flow and shocks in narrow injector passages and in near-nozzle regions. In general, there is a lack of well-established and validated practices for the modeling of high-pressure H 2 DI in large-bore engines. Here, to this end, this study focuses on computational fluid dynamics (CFD) modeling of the mixture formation process in a heavy-duty optical engine employing a medium-pressure H 2 DI system. Both large eddy simulations (LES) and Reynolds Averaged Navier–Stokes (RANS) simulations are performed and evaluated against optical data. Gaseous hydrogen is injected into the combustion chamber via a centrally located outward opening hollow-cone injector at a pressure of 40 bar. Simulations are carried out for two injection timings, namely, −120 and −60 °CA. The numerical predictions for H 2 distribution in different horizontal and vertical planes during the compression stroke are systematically compared against optical data obtained through planar laser-induced fluorescence (PLIF) measurements. Overall, the LES approach using the Dynamic Structure model is found to have good predictive capabilities for the early jet penetration in terms of length and shape, as well as the later H 2 distributions. However, the unsteady RANS approach with the renormalization group $k - ϵ$ model, which is widely used by industry to model heavy-duty ICEs, significantly underpredicts the H 2 mixing, even at similar mesh resolution to that used in LES. These results indicate that there is a need for the improvement of mixing submodels within the RANS approach when applied to H 2 DI simulations.
LES↗
Numerical simulation of involute-plate research reactor flow behavior using RANS, LES and DNS
This paper investigates the flow behavior of involute-plate research reactors by performing Reynolds-Averaged Navier Stokes simulation (RANS), Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) of the channel flow between fuel plates. By modeling turbulence with different numerical approaches, this study provides data with three levels of fidelity. For the RANS simulation, three widely used turbulence models, i.e., k-ε, k-ω, Reynolds Stress Turbulence model (RST) are applied by using the commercial CFD code STAR-CCM +. For LES and DNS, the open-source CFD code, Nek5000, is used given its outstanding scalability on High Performance Computer (HPC) and high-order technique. The results from RANS simulations are compared with that from LES and DNS for benchmarking. Both macroscale parameters and turbulence statistics, such as velocity magnitude, lateral velocity and turbulence kinetic energy, are presented and analyzed. The results from RANS simulation achieve good agreement with LES and DNS on velocity and turbulence kinetic energy prediction. The RST turbulence model predicts the most similar flow pattern of lateral velocity as compared to LES and DNS. The Lambda-2 (λ2) criterion with a reasonable threshold is used to demonstrate the instantaneous vortices distribution in the involute channel from both LES and DNS calculation. The DNS simulation captures more detailed turbulence especially near the corner, which explains the discrepancy between LES and DNS results near the corner. The normalized RMS error are defined and calculated to assess the performance of those turbulence models. The RST model captures the anisotropic feature of turbulence, which enable it to outperform other turbulence models for predicting the flow behavior in an involute channel. Although some discrepancies are found between LES and DNS results in the corner, the overall deviations between LES and DNS are found to be small. In conclusion, given that the computational cost of DNS calculation is an order of magnitude higher, using LES data for benchmarking RANS model is a cost-effective approach.
DNS↗
A review of computational methods for studying oscillating water columns – the Navier-Stokes based equation approach
This review evaluates the state-of-the-practice numerical tools used to predict the performance of Oscillating Water Column (OWC). The OWC is a widely studied Wave Energy Converter that provides a reliable form of renewable form of electricity that can potentially meet global energy needs. However, the fluid-flow phenomena affecting its hydrodynamic performance are not fully understood. While there has been the successful full-scale deployment of OWCs, various computational methods are being explored to optimize this technology. Potential flow theory is commonly used to evaluate the efficiency of OWCs; however, this assumption tends to over-predict the hydrodynamic performance. Recently, numerical studies using a diverse set of commercial, open-source, or in-house Computational Fluid Dynamics (CFD) using Reynolds Averaged Navier Stokes (RANS) and Large-Eddy Simulation codes show a better comparison to available experimental results but are computationally expensive. ANSYS Fluent was found to be the most widely used CFD code applied to the study of the OWCs, with a high degree of accuracy in terms of experimental validation of numerical results.
16 TIDAL AND WAVE POWER↗
CFD Predictions of Soot & CO Emissions Generated by a Partially-Fueled 9-Element Lean-Direct Injection Combustor
A study was undertaken to investigate the CO & soot emissions generated by a partially-fueled 9- element LDI (Lean-Direct Injection) combustor configuration operating in the idle range of jet engine conditions. In order to perform the CFD analysis, several existing soot/chemistry models were implemented into the OpenNCC (Open National Combustion Code). The calculations were based on a Reynolds-Averaged Navier Stokes (RANS) simulation with standard k-epsilon turbulence model, a 62- species jet-a/air chemistry, a 2-equation soot model, & a Lagrangian spray solver. A separate transport equation was solved for all individual species involved in jet-a/air combustion. In the test LDI configuration we examined, only five of the nine injectors were fueled with the major pilot injector operating at an equivalence ratio of near one and the other four main injectors operating at an equivalence ratio near 0.55. The calculations helped to identify several reasons behind the soot & CO formation in different regions of the combustor. The predicted results were compared with the reported experimental data on soot mass concentration (SMC) & emissions index of CO (EICO). The experimental results showed that an increase in either T3 and/or F/A ratio lead to a reduction in both EICO & SMC. The predicted results were found to be in reasonable agreement. However, the predicted EICO differed substantially in one test condition associated with higher F/A ratio.
Raju, M. S.↗