Benchmark Specifications Research Articles

Forecasting Oil Prices with Non-Linear Dynamic Regression Modeling

The recent energy crisis has renewed interest in forecasting crude oil prices. This paper focuses on identifying the main drivers determining the evolution of crude oil prices and proposes a statistical learning forecasting algorithm based on regression analysis that can be used to generate future oil price scenarios. A combination of a generalized additive model with a linear transfer function with ARIMA noise is used to capture the existence of combinations of non-linear and linear relationships between selected input variables and the crude oil price. The results demonstrate that the physical market balance or fundamental is the most important metric in explaining the evolution of oil prices. The effect of the trading activity and volatility variables are significant under abnormal market conditions. We show that forecast accuracy under the proposed model supersedes benchmark specifications, including the futures prices and analysts’ forecasts. Four oil price scenarios are considered for expository purposes.

Tourism Demand in the Face of Geopolitical Risk: Insights From a Cross-Country Analysis

This paper develops a novel Bayesian heterogeneous panel vector autoregressive model (B-HP-VAR) that quantifies the impact of geopolitical risk shocks on the tourism industry of 14 emerging market and developing economies (EMDE). We find that increasing geopolitical tensions have a persistent negative effect on tourism demand in most of these countries, as shown by our impulse response estimates. Furthermore, evidence from forecast error variance decomposition reveals that geopolitical risk shocks in many EMDE economies constitute the main driver of tourism demand. Analysis from historical decompositions demonstrates that geopolitical tensions have been particularly influential in driving tourism demand in Ukraine, Russia, Turkey, China, Indonesia, Thailand, Colombia, and Mexico. Our main findings are robust to several perturbations to the benchmark specification. Our results have several important implications for policymakers in their efforts to strengthen the ability of the tourism industry to absorb shocks from geopolitical tensions.

Typical Benchmark Specifications for Designing Stable Variable Filters Using Novel Unity-Bounded Functions

This paper first presents six (6) types of benchmark amplitude design specifications, including variable low-pass specification, variable high-pass specification, variable bandpass specification, variable bandstop specification, variable notch-frequency specification, and variable notch-width specification. The design specifications can be taken as typical benchmark specifications targeted at testing as well as validating the effectiveness of various newly developed algorithms for designing various typical variable filters (VFs). Then, the paper provides a novel unity-bounded (UB) function developed for ensuring the stability of those VFs with recursive structures. Finally, a wide variety of simulation results are included for validating the design performance as well as the assured stability by using both the benchmark design specifications and the novel UB function.

Modeling of OECD/NEA P2M Benchmark Cases by Means of TRANSURANUS Code

This paper presents the fuel performance simulation of two past experiments that are concerned with partial fuel melting. The activity is conducted in the framework of the Organisation for Economic Co-operation and Development/Nuclear Energy Agency Power to Melt and Maneuverability (P2M) – Second Framework for Irradiation Experiments (FIDES-II) Joint ExpErimental Programme (JEEP). The xM3 transient experiment involved uranium oxide (UO2) fuel with a ZIRconium Low Oxidation alloy (ZIRLO) cladding base irradiated up to 27 MWd/kg U, followed a ramp test up to 70 kW/m leading to inception of melting. The High Burnup Chemistry Experiment 4 (HBC4) transient was performed according to a rapid power ramp, on a UO2 fuel with a Zircaloy-4 cladding base irradiated up to 47 MWd/kg U. The linear heat generation rate reached 66.3 kW/m at the end of the transient leading to inception of melting and rodlet failure. Three main cases per each of the two transients were modeled with the TRANSURANUS code version 2015 in agreement with the benchmark specifications to consider uncertainty on the power during the ramping phases. The paper presents and discusses the analysis of these tests and provides an in-depth sensitivity analysis to assess the capabilities of the code as well as the effect of user choices in simulating the base irradiation and the inception of melting in light water reactor fuel rods.

A Review of Candidates for a Validation Data Set for High-Assay Low-Enrichment Uranium Fuels

Many advanced reactor concept designs rely on high-assay low-enriched uranium (HALEU) fuel, enriched up to approximately 19.75% 235U by weight. Efforts are underway by the US government to increase HALEU production in the United States to meet anticipated needs. However, very few data exist for validation of computational models that include HALEU, beyond a few fresh fuel benchmark specifications in the International Reactor Physics Experiment Evaluation Project. Nevertheless, there are other data with potential value available for developing into quality benchmarks for use in data- and software-validation efforts. This paper reviews the available evaluated HALEU fuel benchmarks and some of the potentially relevant benchmarks for fresh highly enriched uranium. It then introduces experimental data for HALEU fuel irradiated at Idaho National Laboratory, from relatively recent irradiation programs at the Advanced Test Reactor. Such data should be evaluated and, if valuable, collected into detailed benchmark specifications to meet the needs of HALEU-based reactor designers.

Benchmarking FFTF LOFWOS Test# 13 using SAM code: Baseline model development and uncertainty quantification

The development and deployment of advanced reactors, such as the sodium-cooled fast reactor (SFR), relies on sophisticated modeling tools to ensure the safety of the design under various transients. The predictive capability of these advanced modeling tools requires validation to garner trust in supporting the licensing of the advanced reactors. For this reason, the International Atomic Energy Agency (IAEA) initiated a coordinated research project (CRP) in 2018 for the analysis of the Fast Flux Test Facility (FFTF) Loss of Flow Without Scram (LOFWOS) Test #13.In this study, we present and discuss the benchmarking efforts of the modern system code SAM on the FFTF LOFWOS Test #13. The SAM baseline model was developed according to the benchmark specification, which included a detailed core model with reactivity feedback. Generally, good agreement was observed between the baseline results and benchmark measurements; however, discrepancies persisted, particularly in predicted fuel assembly coolant outlet temperatures. Utilizing the baseline model, uncertainty quantification (UQ) and sensitivity analysis (SA) were conducted with the assistance of various statistical learning and machine learning methods, including kernel density estimation, Gaussian processes, and Sobol indices. Following the baseline model prediction and UQ and SA results, we discuss the reasons for the simulation discrepancies and propose further improvements to the model. This benchmarking effort adheres to the best-estimate plus uncertainty approach and can serve as a valuable example for supporting risk-informed licensing of advanced reactors.

Coupled 3D neutronics/thermal-hydraulics analysis of Superphénix start-up tests with DYN3D/ATHLET code system

The capabilities of the DYN3D/ATHLET coupled code system were recently extended to allow for 3D neutron kinetics/thermal hydraulics analyses of Sodium cooled Fast Reactors at reactor system level. In this paper, the DYN3D/ATHLET coupled code system was applied to analyze the Superphenix reactor transient benchmark. The benchmark comprises six operational transients initiated during the SPX reactor start-up tests, covering a wide range of reactor operating states. The peculiarity of these transients is the necessity to consider thermal expansions of major structural elements of the primary system, which significantly influence the transient position of control rods in the core. The paper includes a brief summary of the benchmark specifications, description of the neutron kinetics and thermal hydraulics models, an analysis of the simulation results, and a comparison to the experimental data.

Migrants and imports: Evidence from Dutch firms

AbstractThis paper examines the effect of hiring migrants on firms' imports using a rich employer–employee dataset from the Netherlands for 2010–17. We use an instrumental variables strategy, and find that firms that employ migrants from a high‐income country are more likely to import from that country. Our benchmark specification indicates that a one standard deviation increase in the share of migrant workers from a certain country raises their employer's probability of importing from those workers' origin country by 6.6 percentage points, explaining half of the average probability of importing from a given country. This result is robust to a battery of sensitivity checks, and the effects are driven largely by migrants working in trade intermediaries that import final goods and inputs. Our results suggest that migrants help to erode informational barriers and enable their employers to source goods from abroad.

Synthesizing MILP Constraints for Efficient and Robust Optimization

While mixed integer linear programming (MILP) solvers are routinely used to solve a wide range of important science and engineering problems, it remains a challenging task for end users to write correct and efficient MILP constraints, especially for problems specified using the inherently non-linear Boolean logic operations. To overcome this challenge, we propose a syntax guided synthesis (SyGuS) method capable of generating high-quality MILP constraints from the specifications expressed using arbitrary combinations of Boolean logic operations. At the center of our method is an extensible domain specification language (DSL) whose expressiveness may be improved by adding new integer variables as decision variables, together with an iterative procedure for synthesizing linear constraints from non-linear Boolean logic operations using these integer variables. To make the synthesis method efficient, we also propose an over-approximation technique for soundly proving the correctness of the synthesized linear constraints, and an under-approximation technique for safely pruning away the incorrect constraints. We have implemented and evaluated the method on a wide range of benchmark specifications from statistics, machine learning, and data science applications. The experimental results show that the method is efficient in handling these benchmarks, and the quality of the synthesized MILP constraints is close to, or higher than, that of manually-written constraints in terms of both compactness and solving time.

Simulation-based studies on graphite absorption properties for ASTRA critical experiments

This work is focused on the improvement of the graphite impurity content characterization for the ASTRA critical facility. The equivalent boron content for the graphite reflectors of the ASTRA facility reported in the benchmark specifications was estimated using the results of the exponential experiments performed by graphite manufacturer. Because raw measured data are not available for these experiments, we tried to reassess the equivalent boron content for the ASTRA graphite reflectors using the results of analogous exponential experiments performed earlier on the empty RBMK critical facility at the National Research Center «Kurchatov Institute» and further simulation of the experiments utilizing the Monte Carlo code Serpent with ENDF/B-VII.0, ENDF/B-VII.1, and ENDF/B-VIII.0 cross-section libraries. The same grade graphite was used for manufacturing the graphite blocks for the RBMK critical facility and the ASTRA critical facility. The Monte Carlo simulations confirmed the accuracy of the exponential measurements performed for RBMK graphite characterization. It was also shown that the simulation must reproduce the graphite absorption cross-section estimated using the exponential experiments for the agreement between the measured and calculated distribution of thermal neutron flux. Therefore, the equivalent boron content should be assessed individually for each cross-section library used in the simulation. In case of the ASTRA facility the carbon capture cross section of 3.36mb at 0.0253 eV (ENDF/B-VII.0) was used to deduce the equivalent boron content of 1 ppmwt from the measured results. Thermal capture cross-section of carbon was significantly increased in the ENDF/B-VII.1 and other recent cross-section libraries. Therefore, when calculating the ASTRA benchmark with these cross-section libraries the equivalent boron content reported in the benchmark specification should be reassessed.

Benchmark Analysis on Loss-of-Flow-without-Scram Test of FFTF Using Refined SAC-3D Models

The Fast Flux Test Facility (FFTF) is a liquid sodium-cooled nuclear reactor designed by the Westinghouse Electric Corporation for the U.S. Department of Energy. In July 1986, a series of unprotected transients were performed to demonstrate the passive safety of FFTF. Among these, a total of 13 loss-of-flow-without scram (LOFWOS) tests were conducted to confirm the liquid metal reactor safety margins, provide data for computer code validation, and demonstrate the inherent and passive safety benefits of specific design features. In our preliminary work, we have performed relatively coarse modeling of the FFTF. To better predict the transient behavior of FFTF LOFWOS test #13, we modeled it using a more refined thermal-hydraulics model. In this paper, we simulate FFTF LOFWOS test #13 with the system safety analysis code SAC-3D according to the benchmark specifications provided by Argonne National Laboratory (ANL). The simulation range includes the primary and secondary circuits. The reactor core was modeled by the built-in 3D neutronics calculation module and the parallel-channel thermal-hydraulics calculation module. To better predict the reactivity feedback introduced by coolant level variations within the GEMs, a real-time macro cross-section homogenization processing module was developed. The steady-state power distribution was calculated as the transient simulation initial boundary conditions. In general, both the steady-state calculation results and the whole-plant transient behavior predictions are in good agreement with the measured data. The relatively large deviations in transient simulation occur in the outlet temperature predictions of the PIOTA in row 6. It can be preliminarily explained by the reason for neglecting the heat transfer between channels in this model.

Nuclear Data Sensitivity Study for the EBR-II Fast Reactor Benchmark Using SCALE with ENDF/B-VII.1 and ENDF/B-VIII.0

The EBR-II benchmark, which was recently included in the International Handbook of Evaluated Reactor Physics Benchmark Experiments, served as a basis for assessing the performance of the SCALE code system for fast reactor analyses. A reference SCALE model was developed based on the benchmark specifications. Great agreement was observed between the eigenvalue calculated with this SCALE model and the benchmark eigenvalue. To identify potential gaps and uncertainties of nuclear data for the simulation of various quantities of interest in fast spectrum systems, sensitivity and uncertainty analyses were performed for the eigenvalue, reactivity effects, and the radial power profile of EBR-II using the two most recent ENDF/B nuclear data library releases. While the nominal results are consistent between the calculations with the different libraries, the uncertainties due to nuclear data vary significantly. The major driver of observed uncertainties is the uncertainty of the 235U (n,γ) reaction. Since the uncertainty of this reaction is significantly reduced in the ENDF/B-VIII.0 library compared to ENDF/B-VII.1, the obtained output uncertainties tend to be smaller in ENDF/B-VIII.0 calculations, although the decrease is partially compensated by increased uncertainties in 235U fission and ν¯.

Machine learning models for forecasting power electricity consumption using a high dimensional dataset

We use regularized machine learning models to forecast Brazilian power electricity consumption for short and medium terms. We compare our models to benchmark specifications such as Random Walk and Autoregressive Integrated Moving Average. Our results show that machine learning methods, especially Random Forest and Lasso Lars, give more accurate forecasts for all horizons. Random Forest and Lasso Lars managed to keep up with the trend and the seasonality for various time horizons. The gain in predicting PEC using machine learning models relative to the benchmarks is considerably higher for the very short-term. Machine learning variable selection further shows that lagged consumption values are extremely important for very short-term forecasting due to the series high autocorrelation. Other variables such as weather and calendar variables are important for longer time horizons.

Thermal-hydraulic transient analysis of the FFTF LOFWOS Test #13

In the framework of a Coordinate Research Project (CRP) endorsed by the International Atomic Energy Agency (IAEA), the Department of Astronautical, Electrical and Energy Engineering (DIAEE) of Sapienza University of Rome has developed a thermal–hydraulic model of the Fast Flux Test Facility (FFTF). FFTF was a Sodium-cooled Fast Reactor (SFR) developed by the Westinghouse Electric Corporation for the U.S. Department of Energy, which operated from 1980 to 1992. The main mission of the reactor was to prove feasibility and operation of SFR nuclear power plants. For this purpose, FFTF was designed as a flexible system and extensively instrumented, making it attractive for code validation purposes. The proposed benchmark exercise involves a Loss Of Flow Without Scram (LOFWOS) test, performed in 1986. DIAEE has developed and is validating a multiphysics modelling based on a Neutron-Kinetic/Thermal-Hydraulic (NK/TH) coupling approach with RELAP5-3D© and PHISICS codes. The present paper deals with the assessment of the standalone TH modelling, establishing the basis for the next NK/TH phase. Steady-state results have been compared with benchmark specifications, proving the adequacy of the model calibration. After that, the comparison between transient simulation and experimental data has highlighted an overall good agreement, demonstrating the capability of the developed TH nodalization to reproduce the FFTF LOFWOS Test #13. Outside of the parameters selected for the comparison in the benchmark exercise, outlet plenum thermal stratification and sodium free surface motion within Gas Expansion Modules (GEM) have been analysed, providing useful outcomes for the understanding of the transient event and for future perspectives.

GraphMineSuite

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by >2X), maximal clique listing (by >9×),k-clique listing (by up to 1.1×), and subgraph isomorphism (by 2.5×), also obtaining better theoretical performance bounds.

Forecasting the intra-day effective bid ask spread by combining density forecasts

ABSTRACT The bid-ask spread refers to the tightness dimension of liquidity and can be used as a proxy for transaction costs. Despite the importance of the bid-ask spread in the financial literature, few studies have investigated its forecastability. We propose a new methodology to predict the bid ask spread by combining density forecasts of two types of models: Multiplicative Errors Models and ARMA-GARCH models. Our method is employed to predict the effective intra-day bid-ask spread series of all shares pertaining to the CAC40 index. Using a one-step-ahead out-of-sample framework, we resort on the Model Confidence Set procedure to classify models and we found that the proposed model appears to beat all the benchmark specifications.

Equilibrium asset pricing with transaction costs

We study risk-sharing economies where heterogeneous agents trade subject to quadratic transaction costs. The corresponding equilibrium asset prices and trading strategies are characterised by a system of nonlinear, fully coupled forward–backward stochastic differential equations. We show that a unique solution exists provided that the agents’ preferences are sufficiently similar. In a benchmark specification with linear state dynamics, the empirically observed illiquidity discounts and liquidity premia correspond to a positive relationship between transaction costs and volatility.

Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms

PurposeThe main purpose of this study is to forecast inflation rates in the case of the Turkish economy with shrinkage methods of machine learning algorithms.Design/methodology/approachThis paper compares the predictive ability of a set of machine learning techniques (ridge, lasso, ada lasso and elastic net) and a group of benchmark specifications (autoregressive integrated moving average (ARIMA) and multivariate vector autoregression (VAR) models) on the extensive dataset.FindingsResults suggest that shrinkage methods perform better for variable selection. It is also seen that lasso and elastic net algorithms outperform conventional econometric methods in the case of Turkish inflation. These algorithms choose the energy production variables, construction-sector measure, reel effective exchange rate and money market indicators as the most relevant variables for inflation forecasting.Originality/valueTurkish economy that is a typical emerging country has experienced two digit and high volatile inflation regime starting with the year 2017. This study contributes to the literature by introducing the machine learning techniques to forecast inflation in the Turkish economy. The study also compares the relative performance of machine learning techniques and different conventional methods to predict inflation in the Turkish economy and provide the empirical methodology offering the best predictive performance among their counterparts.

BEAVRS BENCHMARK EVALUATION WITH CASMO5 AND SIMULATE5

The MIT BEAVRS benchmark problem, which was primarily setup for the verification and validation of high-fidelity tools that have coupled neutron transport, thermal-hydraulics, and fuel isotopic depletion models, has also found extensive usage in the reactor physics community for validating core analysis tools. The primary purpose of this paper is to provide an accurate, comprehensive evaluation of the BEAVRS benchmark with CASMO5 and SIMULATE5 codes. The CMS5 calculated results for low-power physics tests (hot zero power critical boron, control rod worth and isothermal temperature coefficients) and full power operation (boron let-down and flux map reaction rate distributions) are compared to plant measured data provided in the benchmark specification. The CMS5 model, using ENDF/BVII.1 nuclear data library, predicts HZP critical boron concentration for all-rods-out conditions within 10 ppm for Cycle-1, and 25 ppm in Cycle-2; the control rod worth is predicted with a difference of 0.7% ± 3.8%, where the maximum difference is less than 10%. For the core follow calculations at the hot full power condition, the average difference in predicting the critical boron concentration is less than 20 ppm. In addition, the radial and nodal reaction rate distributions are predicted with a mean difference of about 1.6% and 3.8%, respectively. The CMS5 calculations are repeated using the most recent ENDF/B-VIII.0 library. No significant difference is observed in predicting measured plant parameters with different nuclear data libraries. Additionally, the impact of various modeling options, which are typically employed with nodal diffusion codes, on the predictions of important core parameters are presented as part of the benchmark evaluation.

OVERVIEW OF THE OECD-NEA EXPERT GROUP ON MULTI-PHYSICS EXPERIMENTAL DATA, BENCHMARKS AND VALIDATION

The OECD Nuclear Energy Agency (NEA) Nuclear Science Committee (NSC) established the Expert Group on Multi-physics Experimental Data, Benchmarks and Validation (EGMPEBV) in 2014 to bridge the gap between advanced, multi-physics simulation capabilities and the relatively low availability of dedicated, high-fidelity experimental data and benchmarks specifically for multi-physics modelling and simulation tools. The EGMPEBV was mandated to establish mechanisms for the certification of experimental data and benchmark models and to establish the processes and procedures for the validation of multi-physics modelling and simulation tools. The EGMPEBV oversees three task forces, covering (1) experimental data qualification and benchmark evaluation, (2) validation guidelines and needs and (3) example application of validation experiments. These have generated numerous reports surveying the state-of-the-art in multi-physics validation, challenge areas and recommendations for the evaluation of multi-physics benchmarks, while in parallel developing the specifications for multi-physics benchmarks. Three benchmark specifications are in active development, including a reactivity compensation scenario in the Rostov Unit 2 VVER-1000, multi-cycle depletion of the TVA Watts Bar Unit 1 and study of pellet cladding mechanical interaction within ramp tests performed at the Studsvik R2 reactor. We provide an overview of the recent progress in these areas and a summary of the future activities of the EGMPEBV in establishing international multi-physics benchmarks.