Pramod Kumar
email: pramod.kumar@univ-evry.fr

Kumar Sarvesh

Pierre Singh

Amir-Ali Ngae

Grégory Feiz

Turbelin

Assessment of a CFD model for short-range plume dispersion: Applications to the Fusion Field Trial 2007 (FFT-07) diffusion experiment

Simulations of the short-range plume dispersion under different atmospheric conditions can provide essential information for the development of source reconstruction methodologies that allows to retrieve the location and intensity of an unknown hazardous pollutant source. This process required a comprehensive assessment of the atmospheric dispersion models with tracer diffusion experiments in various stability conditions. In this study, a comprehensive evaluation of a CFD model fluidyn-PANACHE is performed with the observations from available seven trials of single releases conducted in the Fusion Field Trail 2007 (FFT-07) tracer experiment. The CFD simulations are performed for each trial and it was observed that the CFD model fluidyn-PANACHE provides good agreement of the predicted concentrations with the observations in both stable and convective atmospheric conditions. A comprehensive analysis of the simulated results is performed by computing the statistical performance measures for the dispersion model evaluation. The CFD model predicts 65.4% of the overall concentration points within a factor of two to the observations. It was observed that the CFD model is predicting better in convective stability conditions in comparison to the trials conducted in stable stability. In convective conditions, 74.6% points were predicted within a factor of two to the observations which are higher than 59.3% concentration points predicted within a factor of two in the trials in stable atmospheric conditions.

Introduction

In case of an accidental or deliberated release of the air pollutants or harmful substances into the atmosphere, it is important to predict where, when and how seriously a released contaminant can affect a specific region. In these situations, the atmospheric dispersion of hazardous contaminants is the first concern for the exposure on surrounding environment and human health, emergency response and risk assessment. The dispersion of air pollutants in the atmosphere is a challenging problem due to the complex interaction of plume with turbulent eddies and complex nature of inhomogeneous sheared turbulence near a rough boundary, meteorological and stability conditions such as wind, temperature inversion and foggy atmosphere [START_REF] Kumar | An analytical model for dispersion of pollutants from a continuous source in the atmospheric boundary layer[END_REF]. Accurate modelling of the atmospheric dispersion of pollutants is also an essential step towards developing the source reconstruction process to retrieve the location and intensity of an unknown release in a specific region [START_REF] Sharan | An inversion technique for the retrieval of single-point emissions from atmospheric concentration measurements[END_REF]Kumar et al., 2015b, etc.).

Over the years, attempts have been made for accurate modelling dispersion of pollutants released from continuous point sources in diverse atmospheric conditions using various types of the dispersion models. Several types of dispersion models, e.g., conventional Gaussian plume models [START_REF] Sharan | A mathematical model for the dispersion of air pollutants in low wind conditions[END_REF]Cimorelli et al., 2005, etc.), dispersion models based on the analytical solutions of simplified form of the advection-diffusion equation [START_REF] Vilhena | A semi-analytical solution for the three-dimensional advection-diffusion equation considering non-local turbulence closure[END_REF][START_REF] Moreira | The state-of-art of the GILTT method to simulate pollutant dispersion in the atmosphere[END_REF]Kumar and Sharan, 2010, etc.), Lagrangian dispersion models [START_REF] Stohl | Technical note: the Lagrangian particle dispersion model flexpart version 6.2[END_REF]Stein et al., 2015, etc.), and hybrid plume dispersion models (HPDM) [START_REF] Hanna | Hybrid plume dispersion model (HPDM) development and evaluation[END_REF], etc. have been frequently used for short-range plume dispersion in diverse atmospheric stability conditions. With rapid advances in computer resources and methods, Computational Fluid Dynamics (CFD) models provide accurate flow fields and pollutant dispersion modelling in diverse regions for various kind of release scenarios that includes the continuous or instantaneous releases from the point, area, volume, and other general sources [START_REF] Liu | On the pollutant removal, dispersion, and entrainment over two-dimensional idealized street canyons[END_REF]Kumar et al., 2015a;Kumar and Feiz, 2016, etc.). The dispersion in the diffusion models with Computational Fluid Dynamics can be model either in Eulerian or Lagrangian particle modelling framework [START_REF] Zhang | Comparison of the Eulerian and Lagrangian methods for predicting particle transport in enclosed spaces[END_REF]). However, it is essential to examine the real predictive capability of the CFD modelling approaches to apply it in emergency contexts of an accidental or deliberate hazardous release in diverse atmospheric conditions. For this purpose, a comprehensive evaluation of the CFD model is required with the concentration

Fusion Field Trial 2007 (FFT-07) field experiment

In this study, we utilized a recently conducted short range (≈ 500 m) comprehensive tracer field experiment at the U.S. Army's Dugway Proving Ground (DPG), Utah in September, 2007 [START_REF] Storwold | Detailed Test Plan for the Fusing Sensor Information From Observing Networks (FUSION) Field Trial[END_REF][START_REF] Platt | Evaluation plan for comparative investigation of source term estimation algorithms using FUSION field trial 2007 data[END_REF]. This highly instrumented tracer experiment is referred to as FUsing Sensor Information from Observing Networks (FUSION) Field Trial 2007 (FFT-07). Most of the existing dispersion datasets measured dosages (time-integrated concentrations), which makes it impossible to replicate the instantaneous concentrations that would have challenged Chemical and Biological (CB) detectors. Only those existing dispersion datasets with high temporal resolution (1 to 50 Hz sampling rate) concentration measurements can be used with signal processing to simulate the responses of current and future CB detectors to real-world challenges. Consequently, the conceptual design for the FFT-07 was to fill the gaps in the real world data needed to validate current and future STE algorithms [START_REF] Storwold | Detailed Test Plan for the Fusing Sensor Information From Observing Networks (FUSION) Field Trial[END_REF]. The STE methodologies developed or validated using the FFT-07 dataset can be a step towards to utilize/improve these techniques to estimate the sources in urban or other geometrically complex regions. The FFT-07 experiment provides high spatial and temporal resolution dispersion and meteorological measurements. It includes the concentration observations from various instantaneous, continuous, single as well as multiple point releases in various atmospheric stability conditions varying from neutral to stable, and unstable conditions.

The design concept of the FFT-07 was to collect data from an abundance of research-grade tracer, sensor, and meteorological instruments rather than employing an "optimal" placement strategy [START_REF] Storwold | Detailed Test Plan for the Fusing Sensor Information From Observing Networks (FUSION) Field Trial[END_REF]. In this experiment, propylene (C 3 H 6 ) was released at 2 m height above the ground surface and the concentrations were measured at 100 fast response digital Photo Ionization Detector (di-giPID) sensors deployed in a rectangular staggered grid of area 475 m × 450 m in 10 rows and 10 columns (Fig. 1). The digiPID samplers were deployed at 2 m height above the ground surface and the position of tracer source was altered according to the ambient wind direction in each trial. The terrain of the experimental site was uniform and homogeneous consisting primarily of short grass interspersed with low shrubs with a height between about 0.25 m and 0.75 m with the momentum roughness length, z 0 = 0.013 ± 0.002 m [START_REF] Yee | Inverse dispersion for an unknown number of sources: model selection and uncertainty analysis[END_REF].

Extensive meteorological and turbulence measurements from many Portable Weather Instrumentation Data system (PWIDs) and ultrasonic anemometer/thermometer (sonic) and other instruments were acquired during this experiment. Three-dimensional sonic anemometers were mounted at five levels [START_REF] Chang | Air quality model performance evaluation[END_REF]4,[START_REF] Peters | Validating a turbulence closure against estuarine microstructure measurements[END_REF]16, and 32 m) on three towers located at grid centre, 750 m north-northwest of grid centre, and 750 m southsouthwest of grid centre. The sonic data from these three towers is processed to produce wind and turbulence statistics and surface fluxes of heat and momentum. In this study, the observations from seven FFT-07 trials 07, 13, 14, 16, 30, 45 and 45 in different atmospheric stability conditions are considered and utilized for the forward dispersion of tracer from a single point release. The atmospheric stability in each selected FFT-07 trial was categorized based on the sign of the Obukhov length (L) computed by the eddy covariance method from the sonic data measured at 4 m level of the centre tower. Other meteorological variables, e.g., wind speed, directions, and ambient air temperature, etc. are observations from a tracer field experiment in different stability conditions.

In order to evaluate the CFD simulations for pollutant dispersion in diverse atmospheric conditions, completeness and reliability of an experimental data is a necessity. Recently, a comprehensive and highly instrumented tracer field experiment called FUsing Sensor Information from Observing Networks (FUSION) Field Trial-2007 (FFT-07) was conducted in September, 2007 at Dugway Proving Ground, Utah, USA for short-range dispersion (≈ 500 m) of the pollutant released from point sources [START_REF] Storwold | Detailed Test Plan for the Fusing Sensor Information From Observing Networks (FUSION) Field Trial[END_REF][START_REF] Platt | Evaluation plan for comparative investigation of source term estimation algorithms using FUSION field trial 2007 data[END_REF]. The main objective of this tracer field experiment was a comparative investigation of the Source Term Estimation (STE) methodologies in different atmospheric and release scenarios [START_REF] Platt | Evaluation plan for comparative investigation of source term estimation algorithms using FUSION field trial 2007 data[END_REF]. The problem of the STE has attracted attention for fast and accurate identification of the unknown accidental or deliberated releases from a finite set of the atmospheric concentration measurements acquired from a deployed monitoring network [START_REF] Pudykiewicz | Application of adjoint tracer transport equations for evaluating source parameters[END_REF][START_REF] Bocquet | Reconstruction of an atmospheric tracer source using the principle of maximum entropy. I: theory[END_REF][START_REF] Sharan | An inversion technique for the retrieval of single-point emissions from atmospheric concentration measurements[END_REF]Kumar et al., 2015b;Kumar et al., 2016, etc.). The STE algorithms are required to use data from networked chemical and biological (CB) and other (e.g., meteorological) sensors to (1) estimate the source characteristics (source location and magnitude), and (2) refine dispersion model predictions of downwind hazards [START_REF] Storwold | Detailed Test Plan for the Fusing Sensor Information From Observing Networks (FUSION) Field Trial[END_REF]. The FFT-07 experiment was designed to fill a key STE validation data gap, the lack of high spatial and temporal resolution dispersion measurements for single and simultaneous multiple source releases. The FFT-07 tracer field experiment includes an extensive set of the meteorological, turbulence, and concentration measurements from single, multiple, continuous and instantaneous point releases in diverse atmospheric conditions. Recently, [START_REF] Singh | Simulation of plume dispersion from single release in Fusion Field Trial-07 experiment[END_REF] and [START_REF] Pandey | Simulation of plume dispersion of multiple releases in Fusion Field Trial-07 experiment[END_REF] evaluated a Gaussian plume dispersion model respectively for single and multiple releases trials of the FFT-07 experiment in which multiple release refers to several releases at the same time from different positions. The FFT-07 dataset completeness and high quality make the experiment an exceptional candidate for the assessment of a CFD model for near-field plume dispersion in different atmospheric stability conditions. The FFT-07 experiment is required to validate, under real-world atmospheric conditions, for forward atmospheric dispersion and the STE algorithms under development to satisfy the requirements for source characterization and hazard prediction refinement.

In this study, a CFD model fluidyn-PANACHE is evaluated with the several available trials of the FFT-07 field experiment in different atmospheric conditions. The CFD model fluidyn-PANACHE is a self-contained fully 3-dimensional (3-D) fluid dynamics, commercial CFD code, designed to simulate accidental and industrial pollutant dispersion in diverse terrains, atmospheric and release scenarios. Three-dimensional numerical simulations for each selected FFT-07 trial were performed for near-field plume dispersion from a single point continuous release using the fluidyn-PANACHE. The dispersion characteristics of the released plume from the CFD simulations are analyzed both qualitatively and quantitatively following the various statistical performance measures for air dispersion model evaluation.

Description of the CFD model fluidyn-PANACHE

A CFD model fluidyn-PANACHE [START_REF] Mazzoldi | CFD and Gaussian atmospheric dispersion models: a comparison for leak from carbon dioxide transportation and storage facilities[END_REF][START_REF]User Manual[END_REF]Kumar et al., 2015a) is utilized in this study for forward plume dispersion from a point release. The fluidyn-PANACHE solves the 3-dimensional (3-D) Reynolds Averaged Naiver-Stokes (RANS) fluid dynamics equations using finite volume numerical techniques. It includes the numerical solutions for both steady and unsteady forms of the RANS equations. In this study, steady RANS solution along with the full conservation equations governing the transport of species concentration, mass, heat transfer and energy for a mixture of ideal gases is utilized for the simulations of flow-field and atmospheric dispersion. In this CFD model, a linear eddy viscosity model of [START_REF] Ferziger | Computational Methods for Fluid Dynamics[END_REF] is used to model the Reynolds stresses. A modified standard k-ϵ turbulence model that solves the transport equations for turbulent kinetic energy k and its dissipation rate ϵ, is used to compute the turbulent structure in domain. In this, the standard k-ϵ model was modified to include the effects of buoyancy and the stability condition of the atmosphere by means of Richardson number, a non-dimensional parameter characterizing the stability of the atmosphere in terms of temperature [START_REF] Mazzoldi | CFD and Gaussian atmospheric dispersion models: a comparison for leak from carbon dioxide transportation and storage facilities[END_REF]. The standard Eulerian advectiondiffusion equation governing the transport and diffusion of air pollutants is solved to simulate dispersion in the FFT-07 tracer experiment. In the CFD model fluidyn-PANACHE, air is treated as compressible and the dispersion is introduced as a passive scalar. In simulations, the Schmidt number (Sc) is taken equal to 0.7.

Numerical setup and simulations

Computational setup and mesh

The study domain for numerical simulations comprises outer and inner domains of width × length of 2000 m × 2000 m and 1000 m × 1000 m, respectively. The inner (nested) domain comprises all the source, sensors and other instruments. To ensure a smoothly varying wind flow over the boundary of inner domain, outer domain boundary was kept away from the main test site consisting all the sensors and instruments and thus, the size of the outer computational domain was considered approximately twice of the inner domain. The heights of inner and outer domains were taken as 100 m and 200 m, respectively. The 3-D unstructured mesh is considered in both outer and inner domains. Mesh is more refined near to the locations of source, receptors and other positions of the instruments. These inner and outer domains discretized into 55 and 60 vertical levels, respectively. In both domains, lowest 39 vertical levels are set with a uniform 0.25 m grid spacing that formed the lowest 10 m of the vertical domains. Above the 10 m level, the vertical grid spacing are set to uniform 2 m (5 vertical levels between 10 m and 20 m), 5 m (4 levels between 20 m and 40 m), 10 m (6 levels between 40 m and 100 m), and, 20 m (5 levels between 100 m and 200 m for outer domain only). The 3-D unstructured mesh in both domains consists 2,599,495 grid cells.

The residuals of all variables were considered to be equal or less than O(10 -4 ) to ensure a converged solution and thus to stop the simulation in a selected trial. The simulated concentrations at each receptor were recorded at one second time interval. For a continuous and constant release in the atmosphere, the simulated concentration-field in the domain becomes invariant after a time. One minute time-average of this invariant concentration-field at the end of a simulation in each trial is considered for evaluation with the FFT-07 observations.

Boundary conditions

Recently, Kumar et al. (2015a) discussed the use of proper inflow boundary conditions in the CFD model fluidyn-PANACHE for dispersion of pollutant in an urban area. The boundary conditions in the present numerical study are constant in time. The lateral boundaries of the domain are treated as inflow and outflow boundaries based on the direction of the mean wind with respect to the domain boundary. The top boundary is treated as an outflow boundary and a no-slip bottom boundary condition is defined at the ground surface where the velocity components are set to zero. Species concentrations are set according to the specified background concentrations. No-flux boundary condition at the ground surface is considered for the concentration filed. It was assumed that the pollutant concentration is negligible at distances far enough from the sources, so that we can impose the Dirichlet type condition for concentration field as the far-field boundary condition. The fluidyn-PANACHE includes the standard wall functions to compute the drag forces on solid walls in the turbulent boundary layer [START_REF] Hanjalic | Turbulence and transport phenomena: modelling and simulation[END_REF], given as

= ⎧ ⎨ ⎩ > < + ++ ++ u
where κ (=0.40) is the von Karman constant, u + = U p /u * is the nondimensional velocity, in which U p is the fluid velocity parallel and relative to wall, y + = ρu * y/μ is non-dimensional wall to cell-centre distance, in which y is the cell-centre to wall distance and μ is the dynamic viscosity of fluid, and E is a function of wall roughness [START_REF] Jayatilleke | The influence of Prandtl number and roughness on the resistance of the laminar sublayer to momentum and heat transfer[END_REF]. The effective viscosity (μ e ) at the wall is computed by μ e = μ max (y + /u + ,1). The inflow profiles were based on the observed surface turbulent fluxes in the atmospheric surface layer. The surface friction velocity (u * ) and the potential temperature scale (θ * ) are computed using the eddycovariance technique with the available fast response observations from sonic anemometers in the FFT-07 field experiment. The Businger-Dyer stability functions [START_REF] Businger | Flux-profile relationships in the atmospheric surface layer[END_REF][START_REF] Dyer | A review of flux-profile relationships[END_REF]i nd i fferent atmospheric stability conditions are utilized for the wind and temperature profiles. These profiles depend on the surface turbulent fluxes and a stability parameter (the Obukhov length (L)) that vary in different atmospheric stability conditions. The computed surface turbulent fluxes and the stability parameter from the available observations during the experimental releases in each trial comprise the effect of different atmospheric stability conditions. At the inflow boundary of the computational domain, velocity, temperature, and turbulence variables are specified as follows:

(a) Wind profiles The mixing length profiles proposed by [START_REF] Paulson | The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer[END_REF] are utilized for inflow boundary condition of wind profiles in stable and convective atmospheric stability conditions. These profiles in different stability conditions are given as follows: For stable conditions:

⎜ ⎟ = ⎡ ⎣ ⎢ ⎛ ⎝ ⎞ ⎠ + ⎛ ⎝ -⎞ ⎠ + ⎛ ⎝ -⎞ ⎠ -⎤ ⎦ ⎥ Uz u κ z z bz L z h κz λ z h z h () * ln 1 2 1 2 0 (2)
For unstable conditions:

⎜ ⎟ = ⎡ ⎣ ⎢ ⎛ ⎝ ⎞ ⎠ -+ ⎛ ⎝ -⎞ ⎠ -⎤ ⎦ ⎥ Uz u κ z z ψ κz λ z h z h () * ln 1 2 m 0 (3)
where b (=4.7) is an empirical constant [START_REF] Businger | Flux-profile relationships in the atmospheric surface layer[END_REF][START_REF] Dyer | A review of flux-profile relationships[END_REF], z is the height above the ground surface, h is the height of the atmospheric boundary layer, and ψ m is the stability correction function for momentum ϕ m =(1-15z/L) -1/4 in unstable conditions by the Businger-Dyer flux-profile relationships [START_REF] Businger | Flux-profile relationships in the atmospheric surface layer[END_REF][START_REF] Dyer | A review of flux-profile relationships[END_REF] and given as [START_REF] Paulson | The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer[END_REF] also computed from the fast response data at 4 m level of the centre tower.

⎜ ⎟ = ⎛ ⎝ + ⎞ ⎠ + ⎛ ⎝ + ⎞ ⎠ -+ - ψ ζ xx x π () l n 1 2 2ln 1 2 2tan ( ) 2 2 1
(4) in which ζ = z/L is the stability parameter and x =(1-15ζ) 1/4 . Here, λ = 0.00027G/f, where G is the geostrophic wind magnitude and the wind profile includes the effect of the ABL height.

(b) Temperature profiles Logarithmic potential temperature profiles (θ) based on the Monin-Obukhov similarity theory are used to describe its vertical variation in neutral, stable, and convective conditions. These profiles are given by

⎜ ⎟ =+ ⎡ ⎣ ⎢ ⎛ ⎝ ⎞ ⎠ -+ ⎤ ⎦ ⎥ θz θ θ κ z z ψζ ψζ () * ln ( ) ( ) hh 0 0 0 (5)
in which ζ 0 = z 0 /L, θ 0 is the surface potential temperature, and the stability correction functions for heat (ψ h ) in stable and unstable conditions are given as follows [START_REF] Businger | Flux-profile relationships in the atmospheric surface layer[END_REF]):

= ⎧ ⎨ ⎩ -- - ⎡ ⎣ ⎤ ⎦ - + ψ ζ Pr ζ β ζ ζP r () (1
)ln( ) , stable ln( ) ln , unstable

t h t δ δ 1 1 (6)
where =-δγ ζ 1 h , in which β h = 7.8, γ h = 11.6, and Pr t = 0.95 is the turbulent Prandtl number (Högström, 1988).

(c) Prognostic turbulence profiles

The vertical profiles of the turbulent kinetic energy (k) and its dissipation rate (ϵ) are required for the proper inflow boundary conditions in the k-ϵ turbulence model. In this study, numerical solution of the one-dimensional (1-D) k-ϵ prognostic turbulence model is used for vertical profiles of k and ϵ in inflow boundary condition [START_REF] Ye | Numerical Evaluation of Wind-induced Dispersion of Pollutants Around Buildings[END_REF]. These prognostic profiles were derived from the following 1-D transport equations for k and ϵ, simplified with the assumptions: (i) constant density (ρ), (ii) steady flow, (iii) neglecting convection, and (iv) neglecting horizontal gradients of all quantities:

⎜ ⎟ ∂ ∂ ⎛ ⎝ + ⎞ ⎠ ∂ ∂ ++-= z μ μ σ k z PPρ ϵ0 t k kb (7) ⎜ ⎟ ∂ ∂ ⎛ ⎝ + ⎞ ⎠ ∂ ∂ ++ - = z μ μ σz k CPC P C ρ ϵϵ [( ) ϵ ]0 t kb ϵ ϵ1 ϵ3 ϵ2 (8)
where σ k and σ ϵ are respectively the Prandtl numbers for turbulent diffusion of k and ϵ, and, C ϵ1 , C ϵ2 and C ϵ3 are the empirical constants. The shear stress-related production of turbulent kinetic energy (P k ), buoyancy-related production of turbulent kinetic energy (P b ), and the turbulent eddy viscosity (μ t ) in Eqs. ( 7)&( 8) are given by

= ⎛ ⎝ ∂ ∂ ⎞ ⎠ = ∂ ∂ = P μ U z Pβ μ Pr g θ z μρ C k ,, a n d ϵ k t b t t t μ 2 2 (9)
where β is the coefficient of thermal expansion, g is the gravitational acceleration. The constants in the k-ϵ turbulence model (Eqs. ( 7)-( 8)) are given as C ϵ1 = 1.44, C ϵ2 = 1.92, C ϵ3 = -0.4,C μ = 0.09, σ k = 1.0, and σ ϵ = 1.3. C ϵ3 does not play a role as buoyancy production in neutral stability conditions and therefore, it sets to zero. C ϵ3 is set to -0.4 in stable condition [START_REF] Peters | Validating a turbulence closure against estuarine microstructure measurements[END_REF]. For the selected vertical profiles for wind and potential temperature (Eqs. ( 2)-( 6)), Eqs. ( 7)&( 8) are solved using a conjugate gradient method.

Methods for the model performance evaluation

The performance of the CFD model is evaluated by computing the statistical performance measures between the observed and simulated concentrations for each selected trial of the FFT-07 field experiment. The computed statistical performance measures, scatter plots, quantile-quantile (Q-Q) plots provides both qualitatively and quantitatively evaluation of the CFD model's performance. The paired simulated and observed concentrations are plotted against each other in a scatter plot and it can be easily visualized the CFD model's over-or under-predictions with respect to the observations. The Q-Q plot begins with the same paired data as the scatter plots, but removes the pairing and instead ranks each of the observed and predicted data separately from lowest to highest [START_REF] Chang | Air quality model performance evaluation[END_REF]. A Q-Q plot is often useful to find out whether a model can generate a concentration distribution that is similar to the observed distribution. [START_REF] Chang | Air quality model performance evaluation[END_REF] provided a detailed analysis of the standard statistical measures for air quality model's performance evaluation. Accordingly, the standard statistical measures such as, Normalized Mean Square Error (NMSE), Fractional Bias (FB), Correlation coefficient (COR), Factor of Two (FAC2), and Index of Agreement (IA) are computed to evaluate the CFD model performance. The positive or negative values of the FB respectively indicate an overall under-or over-prediction from the observations. The NMSE characterizes the scattering in a sample. The ideal values of NMSE = 0 and FB = 0; and COR = 1, FAC2 = 1, and IA = 1 for a prefect model [START_REF] Chang | Air quality model performance evaluation[END_REF]). However, NMSE < 4, -0.3 < FB < 0.3, and FAC2 > 0.5 were the suggested bounds of these measures for an acceptable model performance by [START_REF] Chang | Air quality model performance evaluation[END_REF]. In computations of the statistical performances measures, the simulated and observed concentrations values which are smaller than the detection limit of the sensors are replaced with the detection threshold value. If both observed and predicted concentrations in a sample were below the detection limit, that sample point was removed and thus, the statistical measures were computed if both concentrations values in a pair of simulated and observed concentrations are greater than the threshold value. The sampling calibration range of the detectors used in the FFT-07 experiment was 0.025-1000 ppmv [START_REF] Storwold | Detailed Test Plan for the Fusing Sensor Information From Observing Networks (FUSION) Field Trial[END_REF][START_REF] Yee | Inverse dispersion for an unknown number of sources: model selection and uncertainty analysis[END_REF].

Results and discussions

Figs. 2 & 3 show the scatter and Q-Q plots between the observed and CFD model predicted concentrations for each selected trial of the FFT-07 experiment. The computed statistical performance measures for each trial are given in Table 1. The scatter plots and statistical measures show that the CFD model's simulated concentrations are in good agreement with the observations in each trial. The scatter plots exhibit that most of the concentrations points are close to one-to-one line in each trial. The Q-Q plots in Figs. 2 (b) & 3(b) show a comparison of the predicted and observed concentrations distributions in each selected FFT-07 trial. Most of the concentration points in these Q-Q plots are roughly along the one-to-one line and it shows the comparable distributions of the simulated and observed concentrations in most of the selected FFT-07 trials. In most of the trials, the higher concentrations in Q-Q plots are more close to one-to-one line than the lower concentrations. However, in some trials, e.g., 13, 30, the distribution of the predicted concentrations are not similar to that observed as it is away from the one-to-one line, especially for the lower concentrations at far away from the source in a homogeneous terrain.

The values of NMSE are smaller than the acceptable upper bound (NMSE < 4) in all the selected trials and small values of the NMSE are observed in most of the trials (Table 1). Minimum value of NMSE (=0.16) is observed in trials 45 and 54. As one can see in the scatter plots (Fig. 2) and the extent magnitudes of NMSE (Table 1) in trials 13 and 30, larger scatteredness in these trials was observed in comparison to the other trials. In most of the trials, the simulated concentrations from the CFD model fluidyn-PANACHE under-predict from the observations (Figs. 2 & 3) and this under-prediction can easily be quantified with the positive values of FB in most of the trials (Table 1). A slightly over-prediction (FB = -0.07) was observed in trial 54 (Table 1). The simulated concentrations have good one-to-one correlation with the observations and as evident with the value of IA (≥ 0.73) in Table 1, the CFD model's predicted concentrations have good agreement with the observed concentrations in each FFT-07 trial. The CFD model predicts maximum 90.9% of concentration points within a factor of two to the observations in trial 45 and minimum 45.7% in trial 13. In most of the trials (except in trial 13), the predicted factor of two concentration points to the observations are more than the acceptable bound 50% (Table 1). It is also visible from the scatter plots in each trial (Figs. 2 (a) & 3(a)) that the higher concentrations close to the source are in good agreement with the higher observed concentrations.

As pointed out before that in some trials, e.g., 13, the CFD code performance is not as satisfactory as observed in other trials and this may be mostly due to the large variability in the ambient wind patterns This behavior of the concentrations can not be simulated by considering the invariant wind direction and speed in the inflow boundary conditions throughout a CFD simulation. The unsteady nature of the wind boundary conditions can affect the dispersion results. Inclusion of the variation of the ambient wind conditions with time is necessary in these dispersion scenarios and the unsteady RANS (URANS) simulations instead of the RANS is required for atmospheric dispersion studies, especially when the variability in the wind is significant during the release. Thus, the URANS CFD solution can be explored in a further study for these dispersion scenarios with significant variability in the wind pattern. Table 3 presents the ratio of the corresponding predicted concentrations to the observed peak concentrations in each selected trial of the FFT-07 field experiment. It can easily conclude from these ratios of the peak concentrations that in most of the trials, the peak concentrations are predicted within a factor of two to the observations. The factor of two ratios of these peak concentrations in unstable atmospheric conditions are two in three trials and three in four trials in stable stability conditions. However, an overall under-prediction of the predicted peak concentrations from the observed peak concentrations is observed in all considered FFT-07 trials.

Overall performance

The overall performance of the CFD model is analyzed by computing the statistical measures with the predicted and observed concentrations combined from all seven FFT-07 trials. Table 2 shows the computed statistical measures from all combined trials. Fig. 5 (a) &(b) shows the scatter and Q-Q plots between the observed and predicted concentrations from all the combined trials. These figures and the computed statistical performance measures show a good overall agreement between the CFD model's simulated and observed concentrations. From Q-Q plot for overall concentrations in Fig. 5 (b), it is visible that the concentration points forming a line that's roughly straight and it exhibited that both sets of the predicted and observed concentrations come from the same distribution.

The NMSE value for all combined trials is 3.39 which is smaller than the accepted bound 4.0. However, the magnitude of the NMSE is large from its ideal value zero which exhibits more scatteredness between the predicted and observed concentrations in Fig. 5 (a). Overall 65.4% of the concentrations points are predicted within a factor of two to the observations. The positive value of FB (=0.27) shows that the CFD model's simulated concentrations has an overall under-prediction from the observed concentrations. A good one-to-one correlation (COR = 0.89) and IA = 0.84 show that the simulated concentrations have overall good agreement with the observations.

Performance in stable conditions

Based on the positive sign of the Obukhov length (L), it was observed that the releases in four trials 07, 13, 14, & 30 were conducted in stable atmospheric conditions. In order to evaluate the performance of the CFD model fluidyn-PANACHE in stable conditions, the statistical performance measures were separately computed for the combined data of the simulated and observed concentrations from these four trials only. The scatter and Q-Q plots for all these trials in stable atmosphere are shown in Fig. 5 (a) & (b) and Table 2 presents the corresponding statistical performance measures. These plots show that the simulated concentrations from the CFD model in stable atmospheric conditions have satisfactory agreement with the observations. This fact is also exhibited from the computed statistical performance measures in stable stability conditions. In stable conditions, the CFD model predicts 59.3% of concentrations points within a factor of two to the observations (Table 2). The NMSE (=3.12) is also within the acceptable bound for the air quality model; however, it is large in comparison to its ideal value zero. The large value of NMSE for all stable trials is due to the larger scatter observed in trials 13 & 30 (Table 1). As mentioned before, it may be due to the large variability in wind directions in these trials that was not accounted in RANS solutions by considering the invariant inflow conditions. The CFD model under-predicts from the observed concentrations (FB = 0.34). The predicted concentrations have a good one-to-one correlation (COR = 0.90) with the observations. The higher and middle concentrations in these trials are well predicted by the CFD model fluidyn-PANACHE (Fig. 5 (a)). However, a more scatter is observed for the lower concentrations at the receptors far away from the source (Fig. 5 (a)). The higher concentrations points in the Q-Q plot in Fig. 5 (b) also exhibits a similar trend of the concentration distribution in stable atmospheric conditions.

Conclusions

In this study, three-dimensional CFD simulations were performed for the short-range plume dispersion in seven trials of the single continuous point release from the FFT-07 tracer field experiment in homogeneous terrain. The atmospheric conditions in these FFT-07 trials were observed in both stable and convective stability conditions. A CFD model fluidyn-PANACHE is utilized and evaluated for the near-field forward dispersion in these seven FFT-07 trials in various atmospheric stability conditions. The statistical performance measures were computed to evaluate the CFD model performance in (a) each trial separately, (b) all combined trials, (c) stable conditions only, and (d) convective stability conditions only. A comprehensive statistical analysis of the CFD model simulated concentrations is performed both qualitatively and quantitatively following various statistical performance measures.

It was observed that the simulated concentrations from the CFD model fluidyn-PANACHE are in good agreement with the observations in both stable and convective atmospheric conditions. The CFD model predicts 65.4% of the overall concentrations points within a factor of two to the observations. An overall under-prediction of the predicted concentrations is observed from the CFD simulations. It was observed that the CFD model is predicting better in convective stability conditions in comparison to the trials conducted in stable stability and this may be mostly due to a large variability in the ambient wind patterns during the experimental release in these stable conditions. In convective conditions, the CFD model predicts 74.6% of points within a factor of two to the observations which are higher than the 59.3% concentration points predicted within a factor of two in all selected trials in stable atmospheric conditions. The large variability in the ambient wind patterns during the releases in some trials in stable atmospheric conditions affected the performance of the RANS CFD solution. Inclusion of the variability of the ambient wind conditions with time is required and the unsteady RANS simulations instead of the RANS may be useful when the variability in the wind is significant during a continuous release.

The CFD simulations and a comprehensive statistical performance analysis and comparison of the results with FFT-07 observations exhibit the practical utility of the CFD modelling for short-range atmospheric dispersion problems in various stability conditions. This study is also an essential part to utilize the CFD model fluidyn-PANACHE with an inversion approach to retrieve the location and intensity of an unknown atmospheric tracer sources in various atmospheric conditions. It was also observed that in stable conditions, the CFD code performance is not as satisfactory as in unstable conditions and this may be mostly due to a large variability of the ambient wind patterns during the release in these conditions. However, in stable conditions also, most of the statistical performance measures are also within the bounds for an acceptable model performance given by [START_REF] Chang | Air quality model performance evaluation[END_REF]. Due to the significant variability in the wind patterns during the experimental campaign, the URANS solution is a necessity in further studies to simulate the dispersion phenomena in these types of variable meteorological conditions with time.

Performance in unstable conditions

The Obukhov length in the remaining trials 16, 45, & 54 are observed to be negative and consequently the tracer experiments in these trials were conducted in convective atmospheric conditions. Fig. 5 (a) & (b) shows the scatter and Q-Q plots between the predicted and observed concentrations combined from these trials in unstable stability conditions. The scatter plot shows that the simulated concentrations from the CFD model in convective conditions are in good agreement with the observations. Most of the concentrations points are close to one-to-one line and the Q-Q plot in Fig. 5 (b) also exhibits the similar trend of the concentrations.

Table 2 shows the statistical performance measures for the concentrations in these combined trials in convective stability conditions. A small value of the NMSE (=0.31) shows that a small scatter is observed between the simulated and observed concentrations. The CFD model predicts 74.6% of the concentration points within a factor of two to the observations. The predicted concentrations are slightly under-predicted (FB = 0.01) from the observations. A good one-to-one correlation (COR = 0.95) and index of agreement (IA = 0.97) is observed between the predicted and observed concentrations from all these trials in convective conditions.

It is also observed that in comparison to the stable stability, the CFD model has good agreement of the predicted concentrations with the observations in convective stability conditions (Tables 1 &2, Figs. 2, 3,& 5). The NMSE (= 0.31) in convective conditions is smaller than that obtained in stable atmospheric stability (NMSE = 3.12) and consequently a smaller scatter between the predicted and observed concentration is observed in unstable conditions. The CFD model predicts more number of concentrations points within a factor of two to the observations in convective conditions (FAC2 = 74.6%) in comparison to the FAC2 = 59.3% in stable stability. Compared to the stable conditions, it is also observed that the predicted concentrations in unstable atmospheric conditions have better correlation and agreement with the observed concentrations.

Fig. 1 .

 1 Fig. 1. Schematic layout of the FFT-07 geometry showing 100 sensor locations. The black filled circles denote the position of receptors. The stars denote the source locations of the selected trials -only one was operational in a given trial.

Fig. 2 .

 2 Fig.2. (a) Scatter and (b) quantile-quantile plots between the simulated and observed concentrations for trials 07, 13, 14 & 30 in stable atmospheric conditions. The solid line is the oneto-one line and the dotted lines are factor of two lines between the observed and predicted concentrations.

Fig. 3 .

 3 Fig. 3. (a) Scatter and (b) quantile-quantile plots between the simulated and observed concentrations for trials 16, 45, & 54 in convective atmospheric conditions. The solid line is the one-to-one line and the dotted lines are factor of two lines between the observed and predicted concentrations.

  during the experimental release. The wind direction variability is observed large in this trial and consequently the multiple concentrations peaks are observed. Fig. 4 (a) & (b) shows the isopleths of the observed and the fluidyn-PANAACHE simulated averaged concentrations in trial 13 of the FFT-07 experiment. In order to plot the contours, the observed concentrations were interpolated from their values on the receptor's locations to other grid points in the domain. Similarly, for an equivalent comparative visualization with the observations, the contours of the predicted concentrations are also drawn from the simulated values on the receptor's locations only. Fig. 4 (b) shows only single peak of the predicted concentrations in direction of the mean wind, which is obvious as the RANS CFD simulation was performed by considering only one mean wind direction throughout the experiment release. However, two peak values of the averaged observed concentrations at different locations in different directions can easily be visualized from Fig. 4 (a).

Fig. 4 .

 4 Fig. 4. (a) Observed and (b) predicted concentrations in trial 13 of the FFT-07 field experiment. In these contours, the concentrations were interpolated from their values on the receptor's locations to the grid points. The star indicates the location of source in this trial.

Fig. 5 .

 5 Fig. 5. (a) Scatter and (b) quantile-quantile plots between the simulated and observed concentrations for overall, all stable, and all unstable trials. The solid line is the one-to-one line and the dotted lines are factor of two lines between the observed and predicted concentrations.

Table 1

 1 Statistical performance measures for the each selected trial of the FFT-07 field experiment.

	Trial no.	NMSE	FB	COR	FAC2 (%)	IA
	07	0.28	0.06	0.83	75.0	0.91
	13	2.14	0.63	0.68	45.7	0.76
	14	0.84	0.24	0.58	64.3	0.73
	16	1.86	0.27	0.71	65.2	0.77
	30	2.76	0.30	0.97	68.8	0.81
	45	0.16	0.21	0.96	90.9	0.97
	54	0.16	-0.07	0.95	76.2	0.97

Table 2

 2 Statistical performance measures for overall, all stable, and all unstable trials of the FFT-07 field experiment.

	Trials	NMSE	FB	COR	FAC2 (%)	IA
	Overall	3.39	0.27	0.89	65.4	0.84
	Stable	3.12	0.34	0.90	59.3	0.82
	Unstable	0.31	0.01	0.95	74.6	0.97

Table 3

 3 Ratio of the corresponding predicted concentrations to the observed peak concentrations in each selected trial of the FFT-07 field experiment.

	Trials	07	13	14	16	30	45	54
	Ratio	0.82	0.59	0.51	0.31	0.44	0.96	0.99

Acknowledgments

The Fusion Field Trail 2007 (FFT-07) database is available at https://fft07-dpg.dpg.army.mil/. The authors would like to thank the Defense Threat Reduction Agency (DTRA) for providing access to the FFT07 field experiment dataset. A sincere thanks to Nathan Platt for helping to obtain the FFT-07 dataset. The authors gratefully acknowledge Fluidyn France for use of the CFD model fluidyn-PANACHE. This work was partially supported by the RAPID project DISCARD (no. 132906009), France.


Dr Sangheon Lee
email: sangheonlee76@gmail.com

Embodying "Double Break" in Musical Structure: Punk Rock and the Waning of the Sense of Historical Time

Musical structure does not always work in the same direction as the musician's political attitude, way of life, lyrics or artworks, not to mention the reception of all these by listeners or their fans. This is because musical structure has its own logic and historical context. Sometimes it leans more towards conformity, sometimes it turns out to be rather progressive, despite the fact that music is never unrelated to all of those 'extra-musical' aspects. The problem is that the potential for expression of the structural aspects of music and sound themselves, that is quite different from, even goes beyond, the linguistic and semantic features around this music, might be ignored due to overlooking this: musical structures and sound materials have their own synchronic and diachronic context that leads to their own meaning and effect, sometimes regardless of the musician's intention.

Usual expressions to describe punk rock, such as "back to basic", "returning to rock'n'roll's stripped-down essentials" (Meager 2022) or even "fundamentalism" (Reynolds 2006), expect punk rock to maintain a certain historical consciousness based on which it is supposed to point to a certain period of the well-established history of musical evolution.

Punk and new wave musicians also used the AABA form (or hinted at it) to subvert norms, reaching for an older form to express their opposition to the musical trends of the times. In fact, this occurred precisely as AABA became unequivocally passé (1976 was the first year in which no No. 1 hit on the billboard charts followed an AABA form). While the AABA form and the bridges that emerged from them were not used in Hard Rock, Progressive Rock, or Disco, they were perfectly suited for the self-consciously simple songs of the Ramones. (« Blitzkrieg Bop, » Dee Dee und Tommy Ramone 1976; « I Wanna Be Well, » Joey Ramone 1977), the Sex Pistols « God Save The Queen, » Cook/Jones/Matlock/Rotten 1977) or those Blondie songs that evoke the Girl Group era (« (I'm Always Touched) By Your Presence

The same holds true when horizontal form of punk rock songs is discussed. Ralf von Appen and Markus Frei-Hauenschild rightly remarks the singularity of using AABA form in early punk rock songs such as "Blitzkrieg Bop" by The Ramones and "God Save The Queen" by The Sex Pistols. It would not be an exaggeration for them to regard this usage of AABA form in the era dominated by Verse-Chorus form as a "subversion of norms". Here are two tables each demonstrating the linear song structure of "God Save The Queen" and "Blitzkrieg Bop" (table 1 and2): Table 1: God Save The Queen But again, like the previous examples, von Appen and Frei-Hauenschild seek to position punk rock on the established history of music and attempts to find meaning from it. It should be underlined, however, that the case of The Ramones goes further.

"Blitzkrieg Bop", after the intro including the famous slogan ("Hey ho, let's go"), follows indeed the classic AABA form. However, the third A1 section, which is identical to the first A1 even in terms of lyrics, is followed immediately by another A2 section, then goes again to the section B, which ends up making a single large "cyclic" form: A1 -A2 -B -A1 -A2 -B -A1 -A2 (the numbers here differenciate the lyrics). It is a totally cyclic form where each unit AAB with the same lyrics and instrumentation ends with the outro, which is also identical to the second and "slogan' part of the intro.

As for "Beat on the Brat" (table 3), the 2 nd track of the album, the scheme centered on the verse-chorus formula (intro (5) -verse (20) -instrumental link (4) -chorus (18), the numbers designating the number of bars) reappears in a literally identical way (even with the same words). A brief interlude between these two units and the outro are nothing more than subtle variations of the intro.

Repetition of lyrics in verses, cyclic and symmetrical form throughout the song, antinarrative, so to speak, with a very short intro or without it at all, rarely employing basic and conventional devices such as change of lyrics, interludes, climax and extension in ending is not an expression of the will to present something new through "subversion", let alone an artistic experiment, and to create a new history that will blossom in the future, but rather an tendency of indifference, even sarcasm toward whatever artistic, artisanal effort.

In this regard, it is natural for this type of praxis to be 'self-mocking'. "Judy Is a Punk", the 3 rd track of the same album, one of their two early songs which include the term "punk" in their titles, along with "Sheena Is a Punk Rocker" (1977), that we will examine later. Here again, the second verse-chorus formula is completely identical to the first.

'Judy Is A Punk' -one of the first examples of the word "punk" in a song -was another similarly warped tale of interpersonal relationships, written about two crazed Ramones fans, local juvenile delinquents. With its finger-clicks and knowing repetition, 'Judy' is deconstructionist post-modernism in its purest, most unconscious form. "Second verse, same as the first," the singer yells, in an inspired moment of knowing self-deprecation. Everett (2002: 60) Just before the second verse, the singer Joey Ramone inserts a cheeky announcement: "Second verse, same as the first", which is originally from Herman's Hermits' 1965 US number one hit "I'm Henry The Eighth, I Am". In this point again, The Ramones go a step further. Just before the third verse, where the lyrics change slightly, the singer goes:"Third verse, different from the first". This deliberate "self-deprecation", as True Everett remarks, which is also related to an attitude of indifference towards any artistic sophistication, that, in my view, best characterizes punk rock, is embodied here in the very form of horizontal structure of a song.

Joey Ramone: "To me, 'Sheena' was the first surf/punk rock/teenage rebellion song. I combined Sheena, the Queen of the Jungle, with the primalness of punk rock. Then Sheena is brought into the modern day: 'But she just couldn't stay/She had to break away/Well, New York City really has it all.'" [the second half of the verse] Everett (2002: 83) When it comes to "Sheena Is a Punk Rocker", released in 1977, it has only two verses with the same lyrics, likewise, portraying 'Sheena', the protagonist of the song, who embraces the life of a punk, like Judy and Jacky in the aforementioned song. Intro-verse-chorus-(and)-postchorus structure is repeated in exactly the same form before the last part of the song offers again the chorus, without the slightest variation, to fadeout, as if being able to repeat infinitely (figure 1). Indeed, the refrain "Sheena Is a Punk Rocker" appears almost 20 times throughout the song, but also "Punk Rocker", 4 times per post-chorus: so the words "Punk Rocker" appear nearly 30 times in the entire song.

As you can see in the transcription of the chorus, the distortion guitar hits three power chords, namely the tonic (C) in the first bar, the subdominant (F) and dominant (G), covering the first and second half of the second bar, respectively. The last two power chords being played on the higher strings of the guitar, not only in terms of the pitch of the notes, their sound highlights the higher register, which thus makes audible the partial tones corresponding to the thirds of F major and G major chord. One could argue that this allows to produce even more 'magnetic' power, so to speak, from dominant chord at the end of the phrase to the tonic at the beginning of the next phrase. Hence, you can't help repeating this refrain! Being hardly aware of what her life will be like after becoming a 'punk rocker', "Sheena" lets this phrase repeat endlessly, a repetition which is like a ritual, or a mantra, in which her way of life, her identity, are constantly legitimized. The strong logic between each of the repetitions gives the impression that only the present is repeated, imposes 'cyclically'. In addition, the fade-out is therefore all the more useful, since the 'ending' is truly unknown:

neither the ending of the song, nor the future of her life.

This 'anti-narrative circular structure' à la Ramones continues to appear in later American punk and hardcore punk songs, with different lyrical themes. 1 However, I do not seek to repeat arguments such as how lyrics in punk rock are expressed through music. Rather, music, or, if you prefer, musical structure, sometimes reaches a point that transcends things like the musicians' attitude, their political stance, lyrical content, etc. It can also lead to a certain philosophical implication that can hardly be derived from all those, so-called 'extra-musical'

'facts' or their reception.

To put in another way, on the one hand, the critical field of the music of Ramones is not to be formulated by being positioned at a specific moment in the established history that is all about the progress, otherwise regression, of musical materials and compositional methods, but 'outside' of this historical process. Or you can say 'from inside', because it is about the musical structure itself in the sense that this music undermines the very foundation of the existing musical structure, regardless of whether or not, and to what extent, the musicians intended to do this.

At this point, if we are to narrow down what can be extracted from horizontal structure of music itself, we can cite these two notions: presentism and nihilism.

Impending disaster has become an everyday concern, so commonplace and familiar that nobody any longer gives much thought to how disaster might be averted. People busy themselves instead with survival strategies, measures designed to prolong their own lives, or programs guaranteed to ensure good health and peace of mind.

[…]

1 A good example is Black Flag's "Wasted" (1978/79), where the protagonist's hedonistic yet desperate lifestyle comes to an abrupt end with the third repetition, with little clues to change. In another version of this song, recorded in late 1979, at the end of the third and last chorus ("I was so wasted"), as all other instruments fade away, Keith Morris, the vocal, murmurs two words that does not exist in the original version: "Still (I) am". In other words, "Wasted" is not intended to be nostalgia or even a reflection tinged with regret on this life that has been "wasted".

Because nothing has changed after all. The present is the same as the past. Whether it's alcohol or drugs, surfing or skateboarding, senseless fights, this wasted life has continued, and it will probably continue. In addition, this anti-or non-narrative circular structure can be seen in several songs on the first EP of D.R.I., band from Houston.

After the political turmoil of the sixties, Americans have retreated to purely personal preoccupations. Having no hope of improving their lives in any of the ways that matter, people have convinced themselves that what matters is psychic self-improvement.

[…]

To live for the moment is the prevailing passion-to live for yourself, not for your predecessors or posterity. We are fast losing the sense of historical continuity, the sense of belonging to a succession of generations originating in the past and stretching into the future. It is the waning of the sense of historical time-in particular, the erosion of any strong concern for posterity-that distinguishes the spiritual crisis of the seventies from earlier outbreaks of millenarian religion, to which it bears a superficial resemblance. [START_REF] Lasch | The Culture of Narcissism: American Life in An Age of Diminishing Expectations[END_REF]Lasch ( [1979]]: 12-13)

First, when it comes to presentism, it was clearly diagnosed by the American historian and social critic Christopher Lasch in the late 1970s, while The Ramones used to play those songsand long before it would be coined as such and theorized by the French historian François

Hartog-, based on historical, social, political, and psychological context of American society.

[PPT] The early music of The Ramones and 80's American hardcore punk, DIY and autoentrepreneurship were based on this 'radical individualism', that Lasch describes here and Dewar MacLeod and Steven Blush reaffirm in the context of hardcore punk:

Punk in postsuburbia did not recreate the youth culture revolts of earlier generations, but reflected instead the fragmentation, isolation, and individualism of the 1970s. While earlier punks had wrestled with the leftover ideals of the sixties, hardcore punks rejected them outright. The transition from punk to hardcore, and from the sixties to the eighties, occurred when new punks no longer held out hope for revolution from within or without. The do-ityourself ethos, for them, provided a retreat from larger social, political, and cultural issues.

Dewar Macleod (2010: 99) Hardcore was more about reclaiming American values than undermining them. The scene's emphasis on anti-corporate rugged individualism inspired young entrepreneurs to book shows, start labels and publish zines.

Steven Blush (2010: 320) It is not the traditional American individualism, which is characterized by promises or deferral of compensation, responsibility, patience, and prospects for the future, but a new type of individualism that has been deprived of the prospect of change in future, the sense of historial time or the consciousness of collectivity, or even the future itself, all these ideals that could still be found in the 1960s counterculture. I would like to call this state of mind by the term double break, that is, a break with the past but also with the future.

The self-made man, archetypical embodiment of the American dream, owed his advancement to habits of industry, sobriety, moderation, self-discipline, and avoidance of debt. He lived for the future, shunning self-indulgence in favor of patient, painstaking accumulation.

[…] In an age of diminishing expectations, the Protestant virtues no longer excite enthusiasm. Inflation erodes investments and savings. Advertising undermines the horror of indebtedness, exhorting the consumer to buy now and pay later. As the future becomes menacing and uncertain, only fools put off until tomorrow the fun they can have today. In a similar vein, in the latest issue of Punk & Post-Punk Journal, Justus Grebe explores the presentism, referring to Hartog's theory, as well as the notion of No Future as the "central chronotype" of punk rock, and "negation of the future", which appears actually in another track of The Ramones' first album, "Now I Wanna Sniff Some Glue":

Presentism as a dominant regime of historicity emerged at the same historical moment and out of the same crises as punk and its chronotype 'no future'.

[…] The first thing to note here is the absence of the future. The song does not express a plan to sniff glue, but an immediate -present -desire to do so. This immediacy, or 'tyranny of the instant', is firstly expressed through the use of the present tense 'wanna' and secondly through the repetition of 'now', the only adverbial of time in the lyrics. Furthermore, it is emphasized by the lack of an intro to the song. As a result, the only time present is the present.

Justus Grebe (2023: 181) If the author cites as examples The Clash's "London Calling" and the German band's Slime's "Deutschland" as well as this song by the Ramones, the lyrical theme and its semantic features in theses songs seem more likely than in other aspects to reflect those notions. In other words, in order to find a starting point to talk about presentism in punk rock, he relies primarily on detailed analysis of lyrics, as if he did for some literary works. Even though he does not neglect structural analysis of music, it is still problematic to seek to find preliminary motifs for interpretation of music, from the lyrics. Music, on the other hand, can work without lyrics or without fully understanding of the lyrics. Needless to say, the meaning of lyrics in sound-driven rock music must be treated at a different level from that in literary analysis. Besides, lyrics are often understood in connection with the songwriters' own history or what seems to be their attitude toward life in general, but the actual musical process relies more on the synchronic and diachronic context of musical materials and structural devices themselves.

Punk rock as music is connected to postmodernism and closely related to 20th century nihilism in that it began by recognizing as vain, irreversibly vain all the ideals, values, goals and meanings that decorated the earlier history of rock music, such as 'big stories', cosmic, philosophical exploration, virtuosity, complexity, sophisticated recording technology and sound innovation, modernism, or simply put 'something new'.

However, there is a different form of loss of meaning than nihilism. Not the state in which there was a strong meaning and it was reduced to nothing (or actively annihilated), but the state in which it has been difficult to discover the original strong meaning itself-I would like to call this 'melancholy', distinguishing it from 'nihilism'. Nihilism always needs a meaning to be annihilated, but if even the meaning to be annihilated cannot be found, we are experiencing the 'inertness of desire' (= melancholy) rather than the 'frustration of desire' (= nihilism) […]The postmodern generation, who witnessed the collapse of the 'big story', lived in the 'age of nihilism', but those born in the world after the 'big story' collapsed lived in the 'age of melancholy'. Iwauchi (2019: 24) As an introduction to the so-called "existential turn" in modern philosophy as an attempt to put an end to postmodern thought and recover the lost "height" (transcendence) and "width" (universality), the emerging Japanese philosopher Shotaro Iwauchi calls our era "the era of the melancholists". To put it simply, nihilism is 'the experience of loss of meaning.' However, there is a different form of loss of meaning than nihilism. In nihilism, there was a 'strong' meaning but it turns meaningless, or is annihilated, whereas in melancholy even the meaning to be annihilated is not found, hence the "inertness of desire" (melancholy) rather than the "frustration of desire" (nihilism).

Iwauchi states, "Nihilists have 'aggressiveness' towards traditional authority and suffer from 'a sense of emptiness' that everything may be meaningless, but for melancholists, the problem is 'boredom' and 'fatigue' resulting from the depression of desire; it is a 'premonition of disillusionment' that the meaning that is currently at hand may soon disappear. In short, the melancholist is living a strange desire: 'I do not want to do something, but I do not want to not do anything, either.'" Iwauchi (25). So nihilism and melancholy are not totally separate, and I would argue, punk rock was, and is, positioned on a certain point between these two.

This anti-or non-narrative circular structure, relying or insisting on repetition of certain number of structural units, in which you have nevertheless verse and chorus with a simple but decent melody and a firmly established, quite unique, even physically challenging, guitar sound (like that of Johnny Ramone), not ever trying however to create a long, complex horizontal narrative as every other musician was doing, isn't this propensity echoing to this expression? :

"I do not want to do something, but I do not want to not do anything, either." However, the irony of punk rock lies in the fact that this stubbornness sometimes leads to something special, meaningful, and even something 'new'. To put it another way, the infinite repetition of the current events, the endlessly imposing present, that is, the punk rock à la Ramones, might be a suggestion, embodied by its musical structure, that just through the repetition of, and the insistence on, the present, could be created something meaningful in the future, paradoxically

Figure 1 :

 1 Figure 1: Sheena Is A Punk Rocker

Table 2 : Blitzkrieg Bop

 2

Table 3 : Beat On The Brat

 3